{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000063.SZ   20241225  36.50  39.12  36.42  38.09      36.81    1.28   \n",
      "1     000063.SZ   20241224  36.66  37.39  35.80  36.81      36.15    0.66   \n",
      "2     000063.SZ   20241223  38.15  38.47  35.91  36.15      37.38   -1.23   \n",
      "3     000063.SZ   20241220  36.80  37.96  36.38  37.38      37.80   -0.42   \n",
      "4     000063.SZ   20241219  34.87  38.02  34.87  37.80      34.56    3.24   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3547  000063.SZ   20100108  43.20  43.98  42.51  43.39      43.21    0.18   \n",
      "3548  000063.SZ   20100107  45.21  45.24  42.73  43.21      45.30   -2.09   \n",
      "3549  000063.SZ   20100106  46.30  46.58  45.20  45.30      46.25   -0.95   \n",
      "3550  000063.SZ   20100105  45.47  46.68  44.93  46.25      45.40    0.85   \n",
      "3551  000063.SZ   20100104  45.04  45.50  44.21  45.40      44.87    0.53   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0      3.4773  3415516.95  1.301628e+07  \n",
      "1      1.8257  2433444.91  8.891881e+06  \n",
      "2     -3.2905  2929979.85  1.078807e+07  \n",
      "3     -1.1111  3995630.43  1.481629e+07  \n",
      "4      9.3750  6040313.74  2.224085e+07  \n",
      "...       ...         ...           ...  \n",
      "3547   0.4200   137477.32  5.920138e+05  \n",
      "3548  -4.6100   186837.08  8.156417e+05  \n",
      "3549  -2.0500   104292.59  4.778352e+05  \n",
      "3550   1.8700   147432.25  6.786645e+05  \n",
      "3551   1.1800   117213.58  5.280424e+05  \n",
      "\n",
      "[3552 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "import tushare as ts\n",
    "pro = ts.pro_api('66b4314cc348e689eb9ca7eb0daa4076ccb9dbd3e1a44273d5c9ba67')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000063.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000063.SZ   20241225  36.50  39.12  36.42  38.09      36.81    1.28   \n",
      "1     000063.SZ   20241224  36.66  37.39  35.80  36.81      36.15    0.66   \n",
      "2     000063.SZ   20241223  38.15  38.47  35.91  36.15      37.38   -1.23   \n",
      "3     000063.SZ   20241220  36.80  37.96  36.38  37.38      37.80   -0.42   \n",
      "4     000063.SZ   20241219  34.87  38.02  34.87  37.80      34.56    3.24   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3547  000063.SZ   20100108  43.20  43.98  42.51  43.39      43.21    0.18   \n",
      "3548  000063.SZ   20100107  45.21  45.24  42.73  43.21      45.30   -2.09   \n",
      "3549  000063.SZ   20100106  46.30  46.58  45.20  45.30      46.25   -0.95   \n",
      "3550  000063.SZ   20100105  45.47  46.68  44.93  46.25      45.40    0.85   \n",
      "3551  000063.SZ   20100104  45.04  45.50  44.21  45.40      44.87    0.53   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0      3.4773  3415516.95  1.301628e+07  \n",
      "1      1.8257  2433444.91  8.891881e+06  \n",
      "2     -3.2905  2929979.85  1.078807e+07  \n",
      "3     -1.1111  3995630.43  1.481629e+07  \n",
      "4      9.3750  6040313.74  2.224085e+07  \n",
      "...       ...         ...           ...  \n",
      "3547   0.4200   137477.32  5.920138e+05  \n",
      "3548  -4.6100   186837.08  8.156417e+05  \n",
      "3549  -2.0500   104292.59  4.778352e+05  \n",
      "3550   1.8700   147432.25  6.786645e+05  \n",
      "3551   1.1800   117213.58  5.280424e+05  \n",
      "\n",
      "[3552 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock00063.csv\")\n",
    "stockname='中兴通讯'\n",
    "stockcode='00063'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3542</th>\n",
       "      <th>3543</th>\n",
       "      <th>3544</th>\n",
       "      <th>3545</th>\n",
       "      <th>3546</th>\n",
       "      <th>3547</th>\n",
       "      <th>3548</th>\n",
       "      <th>3549</th>\n",
       "      <th>3550</th>\n",
       "      <th>3551</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "      <td>000063.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>36.5</td>\n",
       "      <td>36.66</td>\n",
       "      <td>38.15</td>\n",
       "      <td>36.8</td>\n",
       "      <td>34.87</td>\n",
       "      <td>32.49</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.58</td>\n",
       "      <td>30.98</td>\n",
       "      <td>31.16</td>\n",
       "      <td>...</td>\n",
       "      <td>48.2</td>\n",
       "      <td>48.49</td>\n",
       "      <td>45.73</td>\n",
       "      <td>43.2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>45.21</td>\n",
       "      <td>46.3</td>\n",
       "      <td>45.47</td>\n",
       "      <td>45.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>39.12</td>\n",
       "      <td>37.39</td>\n",
       "      <td>38.47</td>\n",
       "      <td>37.96</td>\n",
       "      <td>38.02</td>\n",
       "      <td>35.62</td>\n",
       "      <td>34.49</td>\n",
       "      <td>31.38</td>\n",
       "      <td>31.06</td>\n",
       "      <td>31.3</td>\n",
       "      <td>...</td>\n",
       "      <td>48.6</td>\n",
       "      <td>48.97</td>\n",
       "      <td>48.78</td>\n",
       "      <td>46.63</td>\n",
       "      <td>44.98</td>\n",
       "      <td>43.98</td>\n",
       "      <td>45.24</td>\n",
       "      <td>46.58</td>\n",
       "      <td>46.68</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>36.42</td>\n",
       "      <td>35.8</td>\n",
       "      <td>35.91</td>\n",
       "      <td>36.38</td>\n",
       "      <td>34.87</td>\n",
       "      <td>32.11</td>\n",
       "      <td>32.97</td>\n",
       "      <td>30.56</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.89</td>\n",
       "      <td>...</td>\n",
       "      <td>46.8</td>\n",
       "      <td>47.85</td>\n",
       "      <td>45.4</td>\n",
       "      <td>43.14</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42.51</td>\n",
       "      <td>42.73</td>\n",
       "      <td>45.2</td>\n",
       "      <td>44.93</td>\n",
       "      <td>44.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>38.09</td>\n",
       "      <td>36.81</td>\n",
       "      <td>36.15</td>\n",
       "      <td>37.38</td>\n",
       "      <td>37.8</td>\n",
       "      <td>34.56</td>\n",
       "      <td>33.16</td>\n",
       "      <td>31.35</td>\n",
       "      <td>30.55</td>\n",
       "      <td>31.28</td>\n",
       "      <td>...</td>\n",
       "      <td>47.3</td>\n",
       "      <td>48.25</td>\n",
       "      <td>48.6</td>\n",
       "      <td>46.61</td>\n",
       "      <td>43.5</td>\n",
       "      <td>43.39</td>\n",
       "      <td>43.21</td>\n",
       "      <td>45.3</td>\n",
       "      <td>46.25</td>\n",
       "      <td>45.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>36.81</td>\n",
       "      <td>36.15</td>\n",
       "      <td>37.38</td>\n",
       "      <td>37.8</td>\n",
       "      <td>34.56</td>\n",
       "      <td>33.16</td>\n",
       "      <td>31.35</td>\n",
       "      <td>30.55</td>\n",
       "      <td>31.28</td>\n",
       "      <td>31.15</td>\n",
       "      <td>...</td>\n",
       "      <td>48.25</td>\n",
       "      <td>48.6</td>\n",
       "      <td>46.61</td>\n",
       "      <td>43.5</td>\n",
       "      <td>43.39</td>\n",
       "      <td>43.21</td>\n",
       "      <td>45.3</td>\n",
       "      <td>46.25</td>\n",
       "      <td>45.4</td>\n",
       "      <td>44.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>1.28</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>3.24</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>0.13</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>1.99</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>3.4773</td>\n",
       "      <td>1.8257</td>\n",
       "      <td>-3.2905</td>\n",
       "      <td>-1.1111</td>\n",
       "      <td>9.375</td>\n",
       "      <td>4.222</td>\n",
       "      <td>5.7735</td>\n",
       "      <td>2.6187</td>\n",
       "      <td>-2.3338</td>\n",
       "      <td>0.4173</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>4.27</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>1.87</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>3415516.95</td>\n",
       "      <td>2433444.91</td>\n",
       "      <td>2929979.85</td>\n",
       "      <td>3995630.43</td>\n",
       "      <td>6040313.74</td>\n",
       "      <td>4320619.96</td>\n",
       "      <td>4936477.42</td>\n",
       "      <td>1101096.38</td>\n",
       "      <td>731001.45</td>\n",
       "      <td>607592.29</td>\n",
       "      <td>...</td>\n",
       "      <td>84860.45</td>\n",
       "      <td>150844.91</td>\n",
       "      <td>275383.14</td>\n",
       "      <td>212282.29</td>\n",
       "      <td>138881.92</td>\n",
       "      <td>137477.32</td>\n",
       "      <td>186837.08</td>\n",
       "      <td>104292.59</td>\n",
       "      <td>147432.25</td>\n",
       "      <td>117213.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>13016279.761</td>\n",
       "      <td>8891881.047</td>\n",
       "      <td>10788069.34</td>\n",
       "      <td>14816285.391</td>\n",
       "      <td>22240849.857</td>\n",
       "      <td>14652219.646</td>\n",
       "      <td>16727885.875</td>\n",
       "      <td>3426348.119</td>\n",
       "      <td>2246666.67</td>\n",
       "      <td>1891577.928</td>\n",
       "      <td>...</td>\n",
       "      <td>402046.7387</td>\n",
       "      <td>729296.8687</td>\n",
       "      <td>1311473.718</td>\n",
       "      <td>967553.3752</td>\n",
       "      <td>604897.5331</td>\n",
       "      <td>592013.8023</td>\n",
       "      <td>815641.663</td>\n",
       "      <td>477835.1887</td>\n",
       "      <td>678664.5218</td>\n",
       "      <td>528042.3546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3552 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0            1            2             3     \\\n",
       "ts_code        000063.SZ    000063.SZ    000063.SZ     000063.SZ   \n",
       "trade_date      20241225     20241224     20241223      20241220   \n",
       "open                36.5        36.66        38.15          36.8   \n",
       "high               39.12        37.39        38.47         37.96   \n",
       "low                36.42         35.8        35.91         36.38   \n",
       "close              38.09        36.81        36.15         37.38   \n",
       "pre_close          36.81        36.15        37.38          37.8   \n",
       "change              1.28         0.66        -1.23         -0.42   \n",
       "pct_chg           3.4773       1.8257      -3.2905       -1.1111   \n",
       "vol           3415516.95   2433444.91   2929979.85    3995630.43   \n",
       "amount      13016279.761  8891881.047  10788069.34  14816285.391   \n",
       "\n",
       "                    4             5             6            7           8     \\\n",
       "ts_code        000063.SZ     000063.SZ     000063.SZ    000063.SZ   000063.SZ   \n",
       "trade_date      20241219      20241218      20241217     20241216    20241213   \n",
       "open               34.87         32.49          33.0        30.58       30.98   \n",
       "high               38.02         35.62         34.49        31.38       31.06   \n",
       "low                34.87         32.11         32.97        30.56        30.5   \n",
       "close               37.8         34.56         33.16        31.35       30.55   \n",
       "pre_close          34.56         33.16         31.35        30.55       31.28   \n",
       "change              3.24           1.4          1.81          0.8       -0.73   \n",
       "pct_chg            9.375         4.222        5.7735       2.6187     -2.3338   \n",
       "vol           6040313.74    4320619.96    4936477.42   1101096.38   731001.45   \n",
       "amount      22240849.857  14652219.646  16727885.875  3426348.119  2246666.67   \n",
       "\n",
       "                   9     ...         3542         3543         3544  \\\n",
       "ts_code       000063.SZ  ...    000063.SZ    000063.SZ    000063.SZ   \n",
       "trade_date     20241212  ...     20100115     20100114     20100113   \n",
       "open              31.16  ...         48.2        48.49        45.73   \n",
       "high               31.3  ...         48.6        48.97        48.78   \n",
       "low               30.89  ...         46.8        47.85         45.4   \n",
       "close             31.28  ...         47.3        48.25         48.6   \n",
       "pre_close         31.15  ...        48.25         48.6        46.61   \n",
       "change             0.13  ...        -0.95        -0.35         1.99   \n",
       "pct_chg          0.4173  ...        -1.97        -0.72         4.27   \n",
       "vol           607592.29  ...     84860.45    150844.91    275383.14   \n",
       "amount      1891577.928  ...  402046.7387  729296.8687  1311473.718   \n",
       "\n",
       "                   3545         3546         3547        3548         3549  \\\n",
       "ts_code       000063.SZ    000063.SZ    000063.SZ   000063.SZ    000063.SZ   \n",
       "trade_date     20100112     20100111     20100108    20100107     20100106   \n",
       "open               43.2         44.0         43.2       45.21         46.3   \n",
       "high              46.63        44.98        43.98       45.24        46.58   \n",
       "low               43.14         42.7        42.51       42.73         45.2   \n",
       "close             46.61         43.5        43.39       43.21         45.3   \n",
       "pre_close          43.5        43.39        43.21        45.3        46.25   \n",
       "change             3.11         0.11         0.18       -2.09        -0.95   \n",
       "pct_chg            7.15         0.25         0.42       -4.61        -2.05   \n",
       "vol           212282.29    138881.92    137477.32   186837.08    104292.59   \n",
       "amount      967553.3752  604897.5331  592013.8023  815641.663  477835.1887   \n",
       "\n",
       "                   3550         3551  \n",
       "ts_code       000063.SZ    000063.SZ  \n",
       "trade_date     20100105     20100104  \n",
       "open              45.47        45.04  \n",
       "high              46.68         45.5  \n",
       "low               44.93        44.21  \n",
       "close             46.25         45.4  \n",
       "pre_close          45.4        44.87  \n",
       "change             0.85         0.53  \n",
       "pct_chg            1.87         1.18  \n",
       "vol           147432.25    117213.58  \n",
       "amount      678664.5218  528042.3546  \n",
       "\n",
       "[11 rows x 3552 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f4cf9cb980>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock00063.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3522 entries, 0 to 3521\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3522 non-null   datetime64[ns]\n",
      " 1   open        3522 non-null   float64       \n",
      " 2   high        3522 non-null   float64       \n",
      " 3   low         3522 non-null   float64       \n",
      " 4   close       3522 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 137.7 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>34.87</td>\n",
       "      <td>38.02</td>\n",
       "      <td>34.87</td>\n",
       "      <td>37.80</td>\n",
       "      <td>33.484</td>\n",
       "      <td>32.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3518</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>36.80</td>\n",
       "      <td>37.96</td>\n",
       "      <td>36.38</td>\n",
       "      <td>37.38</td>\n",
       "      <td>34.850</td>\n",
       "      <td>32.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3519</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>38.15</td>\n",
       "      <td>38.47</td>\n",
       "      <td>35.91</td>\n",
       "      <td>36.15</td>\n",
       "      <td>35.810</td>\n",
       "      <td>33.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3520</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>36.66</td>\n",
       "      <td>37.39</td>\n",
       "      <td>35.80</td>\n",
       "      <td>36.81</td>\n",
       "      <td>36.540</td>\n",
       "      <td>34.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3521</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>36.50</td>\n",
       "      <td>39.12</td>\n",
       "      <td>36.42</td>\n",
       "      <td>38.09</td>\n",
       "      <td>37.246</td>\n",
       "      <td>34.713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3517 2024-12-19  34.87  38.02  34.87  37.80  33.484  32.334\n",
       "3518 2024-12-20  36.80  37.96  36.38  37.38  34.850  32.942\n",
       "3519 2024-12-23  38.15  38.47  35.91  36.15  35.810  33.451\n",
       "3520 2024-12-24  36.66  37.39  35.80  36.81  36.540  34.019\n",
       "3521 2024-12-25  36.50  39.12  36.42  38.09  37.246  34.713"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20013 (\\N{CJK UNIFIED IDEOGRAPH-4E2D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20852 (\\N{CJK UNIFIED IDEOGRAPH-5174}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36890 (\\N{CJK UNIFIED IDEOGRAPH-901A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35759 (\\N{CJK UNIFIED IDEOGRAPH-8BAF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  45.04  45.50  44.21  45.40\n",
      "1    2010-01-05  45.47  46.68  44.93  46.25\n",
      "2    2010-01-06  46.30  46.58  45.20  45.30\n",
      "3    2010-01-07  45.21  45.24  42.73  43.21\n",
      "4    2010-01-08  43.20  43.98  42.51  43.39\n",
      "...         ...    ...    ...    ...    ...\n",
      "3547 2024-12-19  34.87  38.02  34.87  37.80\n",
      "3548 2024-12-20  36.80  37.96  36.38  37.38\n",
      "3549 2024-12-23  38.15  38.47  35.91  36.15\n",
      "3550 2024-12-24  36.66  37.39  35.80  36.81\n",
      "3551 2024-12-25  36.50  39.12  36.42  38.09\n",
      "\n",
      "[3552 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3552 entries, 0 to 3551\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3552 non-null   datetime64[ns]\n",
      " 1   open        3552 non-null   float64       \n",
      " 2   high        3552 non-null   float64       \n",
      " 3   low         3552 non-null   float64       \n",
      " 4   close       3552 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 138.9 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>45.04</td>\n",
       "      <td>45.50</td>\n",
       "      <td>44.21</td>\n",
       "      <td>45.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>45.47</td>\n",
       "      <td>46.68</td>\n",
       "      <td>44.93</td>\n",
       "      <td>46.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>46.30</td>\n",
       "      <td>46.58</td>\n",
       "      <td>45.20</td>\n",
       "      <td>45.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>45.21</td>\n",
       "      <td>45.24</td>\n",
       "      <td>42.73</td>\n",
       "      <td>43.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>43.20</td>\n",
       "      <td>43.98</td>\n",
       "      <td>42.51</td>\n",
       "      <td>43.39</td>\n",
       "      <td>44.710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>34.87</td>\n",
       "      <td>38.02</td>\n",
       "      <td>34.87</td>\n",
       "      <td>37.80</td>\n",
       "      <td>33.484</td>\n",
       "      <td>32.334</td>\n",
       "      <td>32.242000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>36.80</td>\n",
       "      <td>37.96</td>\n",
       "      <td>36.38</td>\n",
       "      <td>37.38</td>\n",
       "      <td>34.850</td>\n",
       "      <td>32.942</td>\n",
       "      <td>32.395333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>38.15</td>\n",
       "      <td>38.47</td>\n",
       "      <td>35.91</td>\n",
       "      <td>36.15</td>\n",
       "      <td>35.810</td>\n",
       "      <td>33.451</td>\n",
       "      <td>32.398333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>36.66</td>\n",
       "      <td>37.39</td>\n",
       "      <td>35.80</td>\n",
       "      <td>36.81</td>\n",
       "      <td>36.540</td>\n",
       "      <td>34.019</td>\n",
       "      <td>32.465333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>36.50</td>\n",
       "      <td>39.12</td>\n",
       "      <td>36.42</td>\n",
       "      <td>38.09</td>\n",
       "      <td>37.246</td>\n",
       "      <td>34.713</td>\n",
       "      <td>32.553667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3552 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-01-04  45.04  45.50  44.21  45.40     NaN     NaN        NaN\n",
       "1    2010-01-05  45.47  46.68  44.93  46.25     NaN     NaN        NaN\n",
       "2    2010-01-06  46.30  46.58  45.20  45.30     NaN     NaN        NaN\n",
       "3    2010-01-07  45.21  45.24  42.73  43.21     NaN     NaN        NaN\n",
       "4    2010-01-08  43.20  43.98  42.51  43.39  44.710     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3547 2024-12-19  34.87  38.02  34.87  37.80  33.484  32.334  32.242000\n",
       "3548 2024-12-20  36.80  37.96  36.38  37.38  34.850  32.942  32.395333\n",
       "3549 2024-12-23  38.15  38.47  35.91  36.15  35.810  33.451  32.398333\n",
       "3550 2024-12-24  36.66  37.39  35.80  36.81  36.540  34.019  32.465333\n",
       "3551 2024-12-25  36.50  39.12  36.42  38.09  37.246  34.713  32.553667\n",
       "\n",
       "[3552 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20013 (\\N{CJK UNIFIED IDEOGRAPH-4E2D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20852 (\\N{CJK UNIFIED IDEOGRAPH-5174}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36890 (\\N{CJK UNIFIED IDEOGRAPH-901A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35759 (\\N{CJK UNIFIED IDEOGRAPH-8BAF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36208 (\\N{CJK UNIFIED IDEOGRAPH-8D70}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\FLY\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21183 (\\N{CJK UNIFIED IDEOGRAPH-52BF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XfNBrA0EQMYb0GRbBhhBEF0ko8SGQ+CAIIoah2i5EvJBQ4uPSlFbHZ97MQTCbIDTVR7BFBEEQ/iEIgjTJGLzXptp4sgV3fXYM5Q0doW4aQQRMQomPZKXY54MH//Bd4B+4DUJNZQRbRRAE4RvXaRZf4uPZnytR2WrGc+vPhLBVBNE5Ekp8iBHSs4GGWuvnXZsj3BqCIAjvuE6z+Oszb+JogoaIPhJKfIjdPHiBl99AEAQRhbjWchFPwXiDNdG0CxF9JJT44EXvDgIvUSIRaA1BEIT/BDrtYodtb/W9E0GEmYQSH5/Wqh2f7dEuAgDwlsg0iCAIwk9cxQfPMP5VtiXLLhGFJIz4qNeb0abSOZYtPI+X+s/EdZOew4qd1RFsGUEQhG8EF68PAYxfwqImKStUTSIA8Bu/A//6EgicOdJNiSkSRnx0GKU3Bs8L+DF/NADg/V6XRqJJBEEQfuOaldnq80FWjUgjvPUihC3rIGz6IdJNiSkSRnwIFunUCiUZIwgilnCNbrFaPiLSFEIOoyHSLYgplJFuQLgQLJxkmScnU4IgYgje5RkmMP5NuxCh5Uhqd6h5E3op1b53JhwkjvjgPFs+tJwx3M0hCIIICJ6T2j54P30+iNDRauTwl1F/AACsVJ5JnKmEIJAwfyuepl0IgohhZC0fNO8SUQwG54trPaOJYEtij4QRH6msVHyI83x0KOmmIQgiurG4WG/9jXYhQgdjMjk+1wk0jgRCwoiPTIWLydLF50PoIGchgiCiF4uL9bZWmwmLawgMEVZ4zmmN4kgIBkTCiA9GLVWl4psGANBYF8bWEARBBIbrM4tnWDy94WyEWkMA0twrZqqhExCJIz6ycyXLvNkl7wcliCEIIorhLe5Wjl+ryWIbSXiLU3BwFEEZEAkjPlzhRXN1ANC6c0eEWkIQBOEbV58PIvJYBBIfnSVxxYfZDKWopssHB5oi1xiCIAgfuEbsEZHn65POisHtP38HoepUBFsTWySs+DBxHHhRSepfsweAa22GhXw/CIKIQsjyEX1sq3Va0F/rdz0OvP125BoTYySs+CgXUsAzzu6fy9Tjnvd34s8f7gTf3hbBlhEEQbhjoerbUYfrRMs3ur4RaUcskjAZTl2pg1ayzOqSccbSDQBgqqqEtk9ZJJpFEAQhi0XG4ZSILK5uHsm8SX5Hwo2EtXzoXcKimOrTjs9mMm8SBBFl8LbnEiuQCIkWagwu10Kjld+RcCNhxcex1GLJsqXD6ThkNJP4IAgiumg3W1+YdLy0FpWgb4Nw+jj49d9AsEVfiMtHsAI9z8KFoEmKdBNihoSddnHl8+4XOD6byPJBEESUYbLwABRI4s1oUzgHOWHLTxDe+xcAgEnSAaPOh3iGhqXMm+FDFMRAeCdhLR/eMHJk1iQIIrqw55RQuk67CAJ4MKhIzgdXcRSAq+VDcFhEiNBCf2X/SSjxcQd7DONrdvrcz1BH4bYEQUQX9npUSrjUqeI4rOw5GfePuR9vGgoBAJWtTsdHBgJAfiJElJFQ4uOKWdPwt+fu87nf+naatyMIIrqw15BztXy0mgV8UHIZAGAN0x1H6zvwxzUVju2swLuHZRAhgf7K/pNQ4gMAFKzvOblcS3sYWkIQBOE/9qkUhYvl479t0rpVP59okSwLYMjyES5oestvEk58+IPWpI90EwiCICTYfT4ULgPcenOGZFkluFTsZkCDYpgg3xr/IfEhg5n+LARBRBmefD4ElwgL9Ykj0u1gaD4gXJD48JuEHmWzO5pQZjzrtt4sULgUQRDRhb16u5v4gPN5peWM0FhM7ttp2iUskPbwn4QWHyPbT4BVq93Wm0HigyCI6MKTz4d4wEvmDFCppOmbrO9SNCoS0UVCiw+eYdBD6/5GYD59EoKB/D4IgogeHJYPl9drTvSylMIZAFfxQdMuYUNoqge/9O/k++EHCS4+WPzm8hG4DJWS9St7XoT6lcsj1CqCIAh37JYP12kXMcmcAVC6W3NpPiA8CAwL4ZcfgYojPvdNdBJafAgMi9TkJMy7ebLbtuVt2RFoEUEQhDz2VB0KL2YMhWBxS/HNsUqYzZyHbxBdQW0xS5Z5uxXKQn9vXyS0+OBZz93nWCp7QxBE9GBxRLt4Fh8WRiFr8l+6ZnvI2pXIuMYm8IxtTGESemj1i4T8C+UbrOnTx7WVe9zH29sFQRBEuLFbPpSM52fT/oxSWfHxFV8QqmZ5RBAEHGvoiOtaWfZIo4uqtgAAvi8YY92gUESqSTFDQr7eL0k6gNPbdqDsN7d53MfbD5wgCCLcWPyYdgEAvqkBQEbI2+OL78qb8fIv1RiUl4SnLukZ6eaElFO6bo7PAgCwJD58kZDiI+Xm29HvqhYwqWke9yHLB0EQ0YSnUFu3/UwmIArGvtX7GwAA+84aItySUGK1fLBJznpgH/eYjJlk+fBJQk67MAzjVXgAgJJSfRAEEQXsq2rBY9+dRLnJGsWi8vFiZKqpdlvXu+VUSNrmjdMtJt87xTh2nw9GrXGsW146xc3pl3CnS5aPTz75BMuXL8fUqVNx6623AgBMJhOWLVuGjRs3wmw2Y9iwYZg7dy4yMjKC0NzwoaB7hyCIKOC2d3+1yQ0tAN9W2RaL+zulhjfL7El0FbvPh8CykBikyHDuk05bPo4ePYpvvvkGPXtK5/LeeecdbNu2Dffffz+eeOIJNDY2YsmSJV1uaLghnw+CIKIB1yeRL/HxefcL3NaxAo8tp1vRarQEsWWEHQvjOs1C44cvOiU+Ojo68PLLL+OOO+5AcnKyY71er8f333+POXPmYPDgwSgtLcW8efNw6NAhHD58OGiNDgVDkqQmQhXVQiCIiLP+RAue+ek09GYaNB001Ab8lb2ZfbB43Rk8/O3JEDQocbFbPiyuobU0fvikU9Mub7zxBkaMGIGhQ4di1apVjvXl5eWwWCwYMmSIY11RURFycnJw+PBhlJWVuR3LbDbDbHaaBBmGQZLNeYcJ8ryZ/Xhyx1147VBsPdGIxetrAAAq3hz084cbb/2NR6i/8cfz663Zh7urT2H2eSUJ0Wcxcv38pNt5Xr/TzdiIGk2m7LaKJmNk/nYCD8aPCJBYu772isK39E/BE3udicUYMH71Idb6G0wCFh8bNmzA8ePH8fTTT7tta2pqglKplFhDACA9PR1NTU2yx1u9ejVWrlzpWC4pKcGzzz6L3NzcQJvmN/n5+bLrTxg1AKziQyPwKCgIf2x8KPDU33iF+htPHAAAtB2vQMG14xxr47vPruyXLGWaW1GjyOr00cL3XDvg+CQwGhQW+P9Mj53ra+3jmN6FwF6nVSknOwvqAP7OsdPf4BGQ+Kirq8Pbb7+Nhx9+GGqZarCdYfr06Zg2bZpj2a4Aa2trwXHBTVHLMAzy8/NRXV0tm4inrq7V8dmiTUJVVVVQzx9ufPU33qD+xi8Wgx5VVVUJ1WdA/o04xdJhe0WSh/Mxmx6J59ov+45jHOP7eR6r17e+qVGyXFdbC0aX7vN7sdpfTyiVSr8NBwGJj/LycjQ3N+Mvf/mLYx3P8zhw4AC++uorLFiwABzHob29XWL9aG5u9hjtolKpoFKpZLeF6mIIgiB7bI4XrUvPjIubAfDc33iF+huHuPQxIfrsAd7H9IWb/4EL4fq7FfBtqGJTAADZ5taAzhsL19dkcfp1KJTSayLwfEDF/GKhv8EmIPExZMgQ/O1vf5Ose+2111BYWIirr74aOTk5UCgU2LNnD84991wAQGVlJerq6mT9PaKN/rnORDEJdh8QRFTDUvSAgwmlmTjuxXjBuUVeSOG/+RTsJVcHuVXu5LEm2JvJcMaQny/c6E1W8cEIPHQal6GUblefBCQ+kpKS0KNHD8k6jUaD1NRUx/rJkydj2bJlSElJgU6nw5tvvomysrKYEB8ZWiVG8LXYwebSvUMQ0QS9DTi4ekg+llU1eNzuS3wIK5YCYRAfFoGxJwAFH4fBH21G6zRSksUIVulivadoF58EPb36nDlzwDAMlixZAo7jHEnGYoU0wRZ5Q886gogaxJ4PloY6CB0GQKONWHsiiULhfVqlQ+nMtlnUXoMzyd0k278qPBdXhKRlUjgBjgsXj9qxvcM6Vui4DvdCcvHY4SDTZfHx+OOPS5bVajXmzp0bU4JDDGNLLiaQ+iCIqIGx/R6FpgZU/nkOoFZD8cpKH9+KU1gGD+5dhlptBt7qc5XH3a46tQ6paiXecxEf/ym7NjziQyQZ+Th8nhpNTssHFEqUGM7ieFKedSOJD58kZG0Xf6BbhyCiB4f4OHoA27P6oYZN9vGNOIZhcW7dXlx5er3X3RTdS4CBw2W38WGYB7GIxUccPlA5izXxnULgAYUCj81y5l9JNOfRzpCQVW294fi50L1DEFEDY3uY79KrsGjo7wAAn0ayQRFiVP0BwJItu617ezVOJTvzRXAc77HAGW8ygdWGdtqKE5znjsfB2GJLBaHgLYBCCYXCKeh4XqA3ex+Q+HDB/nOJv58KQcQuu5V5WHe8GVWGxHpkuQ7a27IHAJk5YP/2DtBQh8I9ClS2mvBswVl8V6vAKVEqjXaBRaoH8cEZjVCGWnxALD5CeqqIYLGF2irAg2EYidiIR7EVbBLrl+wXtiqFEW4FQRBOTqiz8PeNVZic4TtFdzwhN13BMAyQngmkZ+If3Xk0GDgUpPbHT59sBsTig1GhjZcXHxaLdNqltt2MHJ0yqGm+LZwFsF2ueJx2sXDWaRelTWiwIvXBR7H4aKprxK4vv0PywMEYPap/xNpBliEX7HPLpD4IIvr4vik4mZVjBYuPQUyjZFGQav2bqFyiYNoZFerM8mKNE/l8/O9QA+Z+cgxv7wi8YJ0nGg0czmoyHMvx4MC//qufMfe/23Gopg2AU8DZc9CwIuHGR7HaOvXJKvydHYyl24N3vTsDiQ8XnNMu0XvzEEQiUNHYEekmRJzPd5yWLF/edsDDnoBaJRUa7YwKyUnyYs3COcXH0m1nAQCfHPCcOyRQ7vuyQrIcD9MQz9fnopbV4dkfKgA4HU6VsCUbE+0bzf0tb7G221cm3FBD4sMFu3gVkHhVBgkimlh7rNnr9mh+wPtCMHZA2LcDgo/6VXuOSFOZensqqZSu4kONm8/vjXHqZjy26z/4cN3/Qclbz8eJpl1C8WdsNEj7FU9Jxsy2vx3v8Plwt3xYotjy8Wa3CwAANUnyjsvhgnw+XHBYPmL4wUYQ8YDFZPa+g8ADPrJ5Riv8q08D+3eAuWw6mOtv87hfq0W6zHmRH2ZW+jjPYYzITFLiLzPOgTB1ENBQC+UPLeCgBKdvd+wXjiddPD1PFbz1ojhCbR3iw7lPPPU3VJDlwwN06xBEZOFqq73vEMuv0/t3AACEH7/0ulsbL31EG+FZbK2tc25LM7XhHvVxxzKTnAKme4nD8mH+5vOAm9wVYvhKuWEXHxaLdZSQEx9VRrKc+4LEhwus7Q6KZochgkgELL4eT7EsPuz46INBkIoNb+JDzF/2voNuGvf1GovVmtRRX+9f+4JEPBkC6pTWSr0Wu+XDlhVbHCm0rZmGVl/QX8iFdFh/nA0nT/vYkyCIUOLLIW7nqabwNCQEmBkFDqX18OkbYHL5Gxi9TDNlJzm3KQUeTFEPt320vMl6nKTUQJrbZWJdJr6zdpfbOouLz4eYcGSQjXVIfLjQrfIwAKA6ws44BJHo+HL6fnJTeN/efXGyyYh2k8X3jgD+PnAW/m/k3fi4+ySv+5kYqR+HkfHspnfrwDTHZ0VBETBynNs+GotNfHTv47ONvCAEzXchln0gOF7Aqlp3M5JDfMjkRuE4/+6DRIbEhwvZrPXH2axOiXBLCCKxMfsQH2wUlS0/Um/AH744jge+PuHX/ptzhwAAPise73EfgefBuVg6vImPHJ1zm/r8ybIJw7Ra6yDaoZO3fHA2S4zZImD+58exeN0Zj+cLhFiexT7WIB/y7XA4lblNVzclcO0hPyHx4YLSVhrZEqNe9AQRL/zSGjsJxdYeaQQAnGkxBfQ91otFwHDssNs6b+JDKQq1VbDyj3atbbXRIi/sHviqAgBwoFaPylYTtp5pgxCEKYRYFR8Haw140IOg5GzRWEoaRTsF/dlcYFXWH3ekE7AQBOEdjo2eTAENVWc79T3GS1xdE+f+DOrw0mdxng+FRsbbFICGsb6td3jQE+WNRusHkVWp7evPPJ7TX45XBS+BWTjZVtnmtm60/gSEYwfxUUs6AKBeLx8SrjfT1Is3aIR1QaVSAQA4RoEfjzfjdIsxwi0iCEKOQU3HIt0EB4ylcwMN48XywSvchYaJUXncX6US7a+Stxppba6fRsH7lBZj0Ds+v7+n6741q5leXT5GJBCaG93WqXkO/MdvO5a35AyW/e67OyObvtwVzkdCu3ATPa8OUQKbngmYgXptBl7YaM0u+OnNkSu+QxCEPL3aKiPdBAfeLBid/R5vcd/WwXoWH0pRbRfBg+VDZQsL5XzMpIjdRc4k5Xrf2QVLHEV6MPp2AElu6yvTCn1+90yrjyR5YeTF93/C9g4d/nl5caSb4oAsHy4oxk5wWycYyfpBENEGn1cU6SY46GxKKW+WDwvvbk3R8J4HNLVaid4tp1DUXoO8QnnBYBcVdnlg9KBCWJGTRqBS4lilu7UAiNGIF4XMECkI+Caln2Mx09gi/10uesTH93wemtQpWPfNZnS3WMsW/CGrLqJtIsuHC6xa5s2CMwMe3iQIgggN57YexS+pnkNChSjyy+qs+GjSpHncxovqrzACj2L9Wcw79imAkbL7swoFnr+qDALHQZksH83CMAwgAAIvYOm2Gnx20F0oCDwPRnAKn91ZZX72xoqiycM0jdEAaHUBHSvisO6BB/XKZGSoWMA2izHv0EoAY92/e+YEgNKQNq8zKGwiMEsRWQsViQ8X5MLTIPMGQhBEaDFn5AJefnrRVLyrs9MugNUiIPfc4S0WwJbR9Kkdr6Jfy0lA6f2RrSjq6XW7Xa7xgiArPACgtaqq0z4sAKBqbwbgLn6E1hYwMSc+3AXuoaQC9GSdAss8dpLj8+zyNXi3dCoAoBypViHnIfIoEjCieu0MG9kU8NHzV4kSZC9IHM1hEkSswPlwioymLJJsV6pBWeQdAVtN1v6lc3qr8ACALogCwPly5U23vfjpDjBdeOGy2HKIpJtaJev3nGmBsG0D+NeXwPLwXRC2b+r0OcKH/D1oOFvj+Nyekun4PHaQM6tsizoFTZs3hq5pnWAdk+8UHxEuP0PiwwVWxpQrdPEHTxBE4Jg8PPjvsD3fo0l8iFsasG+DhyiEOpP1OCVmUZhqUteSV9nfrby18df0PhBcBFEgz0C7sFEIPM5jnBaCx/dZ8MtHn0PYsg6oOQP+taf9b3iEUHjweFEYnYnHzstyXn3V5Csk+2090RSSdnWWQ0yGI3Ow3FgXTkh8uCBn/jT5cg0nCCLoyE2rZGsYKG1+WWKfiEgjeZD6IYqyeWcoq+DBMdEe7aKBBeyfFgFFPcHe+1hXmul4vvnSR27PvADCNO2ikGUY6DROnwkeDJ4Zciu+yx/t97EijcqDRUvDWPvYq60SKSPHOPdXSafF3uK8T4NFAseVJctHdCE37fLDwRqZPQmCCCVylo2B+alQ2MVHtPp8+CE+xE+ZzYeqZfexh6wqALADhkHx+MtgSvvJ7usvrH3axcc0kbG5WbLsagnxhj1CWAEBY3pmum1/pf9M/P7c/8OR1O5+HzNSKBn5a2lWWgMQxusMYBTizLLS8cMIBcxRJJLFsBGedyHx4QIjIwfP7tgZ/oYQRIJjkXlo3zk2H6wtEaAlimq7iMWHPwO1+ClT+es22X3s4kuudkhnsY83n8K94q2Yjq8/dWmL/9MudosVK/A4d1QZlDLfrdNm4vlBs2W/b+QsUROWe6BWvq6LPd+KWi2NhlG6XCsLw+L3n5aHpG3+Iv5bai1G8JE2edgg8eGCnGeymSoUEkHk3yt+wh+WrkdHY1OkmxLVyA1AqRqFo25JtDxEAemDVDwdJNSfBf/6EggVRyT7i8XKboU1J8fW063YXd0OADBZeKyq0wIIrvjw5223uL0GW3IGSdbxATwDedt1U8AaxTO84ZDsfnVad6tIdasJE/+xDi+vO+73+ULJJpN8KPQhnTXJmEbpIj5kLlaDgYMQwYhJsYFw6ukNjs9shKNwSHy4IHc9XCtLEkRXWGPOw0ltDjZ+sz7STYlqPImLaBQfYjEhFh/8G0sgbFkHfvGfpPuLhNXOpGI0GTgsWncGj3x3ChzH4cM99WiwWJ87wYyI9KY9LtBYp1qyLO34qZs0lwgfgMOpfdqFtXkX/Joz0O/vfvLrCVgE4NvTRnQYoydJlyv1aqsoUbmIDZ1KfqwQ9mwPeZtc4TgO1SfOOMQgAKh4zuHzwVKobbQhY/kg8UGEACaKojWiEU9/HftDM7rEhxNLnchHrPo0ONmoAmnb61qd5n3D6VPYV+0saKbsShiv61m9qA+t7THXIVM/xh/LhyAI+PZYEypsvrQKP6ZOWo0uxxUlKHvnk+gKU5VDLZMBdUjjEbd1pmX/hFB9JhxNcvD3937CHetb8cP3Wx3rmNR0R7SLt3shHJD4cCFJLSc+6M9EBJ9gDirxhiAIOK3Okt3mKj5On6rGp5+ug7GtPWztc8VocIqHXz9Z4/h8NLkIN01YjJU9Jkv2d61orxd938zxsDQ6B2GdRd7voDN4m3bR2v6uRpnKuRY/xMemU614+ZdqvHXa+n3Wj8TsL361T7Is6FIcn9dw3Xx+P9IoZMSHISPPbV1rBwf++f8LR5McbFBap4ZWnhatVKtFScZo2iWq0KmVKG09LVkXKstHdauJyi4nGOLwUZYh8eGJs80Gj9sc0y62gXT+T014s60bPl/1XVjaJoe53lkn46jgzO75eo/LYGEVWF46RbI/7/LobT1bJ9rGSAZ7fRB9g7y97Gpt3pLNqhS3bf44nFY0SmtgsX5YPra2SavvRomfKQCgXu972kepdB9C9boMt3Vzxz2CTerIFHUTJ8BjALJ8RC0siytP/SxZlRSCv1Jliwl3fFaO21YdDf7BiajFZHI+0Mjy4Zk2o3vEyDm1ewA4wxldB/BjrZGcxnJeyxaVMxGYxcOLi8Vl2uXHDXud24xGiAvari08N0htBNrNnu+5ZJW1TU1q99TovB+5jg7XSDOaKmx/kzJdANclitSHWaaqsCtKpfv1beflB4znB/+my23qDIwoKkyA4LBHMZRkLLpgGAaYNFWyrmd28OsR7LR5tXdw0fNjI0KP8Rmn6bVLKbnjHLlqr3ceXgXA6aXPu7y5pVs8W0vCSQrvtABYRA94fstPEI7ut653ER9bcgc7PnPrvgIn2t6zrSpobfvCmONxW7bG83DA+0gydry2FTtqTZJ19uygA9L9H2Z+OhtblmClTK2d2cPlKwpHCkkklmgp0t4EJD5kKCqQzjWHQh9sOtnqeyci7jDWnXV89scsnajIhdkm/eYuAJ4dTj1lowwPzrZkc05nUXGk3J27FfjkvS8gCIKk7a4OipbTJ9AM53TELeVrECxcBZsYlYdIDQCw+Jh2OXbCPRGj/f5m/MzH0tzBQa+InurhnB9J7FQylo9L+2SgTBU8P52uIslB01jvuPdo2iUKKevbA/fvew8lrVbv5FBkV99do/e9ExF3mEXOfM8be+PUunURbE30wsk4OKr7WUM27dMuFoZFeYPzIS9E0IGOT00XtcM5INWonetrkrLxdp8rYWlvk4iPPZl9JcfaktoHrQqtYzkzOyNo7cznPTvlqlWeK+b6mnYxw30Qtk+7KP0QHyeajPjNx9IpaC1n9LB3eJATH+NTpKJC4aHKcK4yeiw4YonBi9aQw2kUwmg0mNAvD/1bKgAAy9ne+JlSrBNBwMg6wxgNSi0WH6Ywbjnk6ikpMq1TBuI8HwcOOLNH8mzk/paCwnldeZE9O9vi/pJhbPf+4pGmk4a6Zl0fPF8B17+qjnNOVSkVnv9+dUePounzjyF4CA/nOtzf9O3Tip6KswHA0NYKAMDLG065b4xwJLVZRnyMzZCuU3qwFkVTEL14epdnWMrzEe2wc/4AZV9ncpy/bWuMYGuIeGFb9gDJcpXO8xx8IuNaD2NMkTMCQyw+hLXONOB6c+Qe+ZIhKd05bZvOuvtKdJjMXnOUJHeXpj5Xy5j2O8tAjdSaMKa9wvFZJmrUwYK2PpjTMgjcVvnEeFxjg9s6x7Sil9om9gHwSJP73ynT2OK5QSGGFwQ8+PUJt/WuobVyPh8AwHpYHwlcfT6c0S5k+YhalGFShp7eJoj4I80cuVwUsYRYfDyTfhwLJhY5lh0+Hwwr8WH4IWdY+BroglhM8BlO8XFY4Z6rpKPDu/Pmc4YSybIyiCHZt18xAldqnGG9WrVzkPRnMKqtcRcZAMDK+IQobO3OSnd32Ffy1r8BBxY71v4oe8xcc+T84mrbzZBz+XATHx7GiFED3Wvn9GuuCEbTAoYR3T8dCpUozwdZPqIWuTz9ISGA1MVEbFOeUhjpJsQEYp+P/hdOkDjHsQpntMtP3UaEvW12BM7seHEQj1O+iqKZzOaAXGNVQRQfKclJ+O115zuWmQJnZVl/BqMas//TDPbDXTSmLy5JacODpc5rWmrzpyvX5ODx2nzZYxqZ6LEe2HGth6L0MMFyYe8Mt3WFhjr3HUOEJJ+QaP2urDIIDFk+op4WITQ3v8VVUpsj61hFhI+vi8ZFugkxgdFg/U0MbToGJlma9MqZZIzFkbSejvXjWw+HrX2C2QT+T3PAL7zPuiyyfPgKYuLNXEDiI9hPIZZhML5HCnqlqzCgIE203n1f10iVLUflfd9krQS2XqoUDO6+ejTOP28QFmMHrj3xPa4vtl5DvTLJYztNMplWwwVTKeODAqfwtaP04LQpl0k2nOUAjCLLodjnozophywfscDaluDn9wCAt7/ZI11hJPFBEGJeOWh1YNyd0dttm6fCchZDGMMbTxwF9G3A6eO2tjixf+YFAVkyDqeChUMg3pSqvv07304PPDChGP+4ohRqjci51eVNeEjjEQgu69YUj4dgcO+T3Pu/QmY6edCsG/Gbu29Cav8BMt+QInbODje8KL29GNdpF4WX8OQpKdJpI14Rvv6YJAnSpPcaZ4tMolDbKEYRorwBn9W5pBQ2Rk9MOBE6hNrqSDchZvBWSdo57SJ9fG3KGwqhSX7QCD7SB7fY8sHzwPflzZi14ggaFO4vMDzHec23kW5yDlo9UpXIznDPOBoMGIaBUu18Frm+CPNKNeSwmNxfluQsHwaZa8gwDJi0TGi08scWY2RVEStF75oEzo7rtIuqe4nsfgAw+7JhuCazHVOzrMnXjKrw5TAxiZJTWVx+JyabCKJQ2yimt9ap3O0OUqGAJ/ER9wgWC/i//j7SzYgLFCKHU1cMNZEJiRePvZwAvLipCgYPuTEEs/dnSbMovfmiC4tC+oaqFr25GzgBz010FkXjwchGnFhk0o7LZSLfpu3uvtJGkhfxcf7ZnQAAo0IVMX84S0a227ru7dVulg+VTG0XO6laNW6bOgqDCq1TW21s+MSHUeQzZfYwzJP4iGKUac6HgMLPLH2+EMzuxYp4mnaJfzjfRaoI/7C/fcpZRw62As2GMPytGQYH0nvhrDbTLWOpr8yYFgvnsJT0FJzZUK/spXXbV6UJraleIwrjrTQI6FfsjM45o8lCoybN7Tum+lq3dXJdTje3ua+0n1freSC+4/qJ1vOwKsASupc+b8hZPhhBcEz52VH44TeRorX6rrQp3K9vqDCJxMcJVabsPuTzEcVc0c950YI2ASNzvRs2bQjW0YloRRCokkuQsIsPo8L97fmJQyx+9/FhnxEnXaVCDywYMQ93nvt/gCBgv8pZz0MuOZUYnrM4Ig7+wuzD5ac34NFdr2NSf/cS8skhFh/iiL4kFy3Xok5BqdoEVzZ+8YPbui0N1pezkU3OVPEqL9ZirVa+X4/0E5DVz+rjYmEUXnOEhBJOxl9FxXNulg85x1JXHOLDi3NtsDHJFGZ0hXw+opjze6ThsVF260dwLpScyfIZQ2lQjk1EN4LMPVTWfiYCLYkd5HIjKG0DQIeM+AAAM6MA9KHNp1IuOvyuKum5fNWCEhdp0+Xm4Pajn2J44xG/3qKDjfhNflSW+/mvzXafEt6oKHBbd0JnDZfdnuFMFe/qayBG6yFxWlGyAipbmncLq4AQIYuhq/bI7mjC3YdWQNGJhG8ptimmNmVS2HxYjDIWdldc/VfCTfQFUkcZxanBTdlc3e7+JnE8tUhmTyK+EHAq2f3NNljTefHGEK4We5S5mKp2N/HbB0xOFIqptRjRISpKduxUHfr0T3H7brBQicbp443SAdrMAzKlThy0isras4NHgsFtYHr2gdbFf2CW5QgA31EhXUGcUl3poj2UPAeV2l3g7czqJ1nmPViZLF6chj0JLZVKIcnoynEW+HZNDT4WF/WxdKwKuGYxKgwMAM/TSXKo1FYrD8cqAY4D1KEvA2Dy4VcEkM9HzCAE6aVEFVlLFxEhLBYB94253209F+m61lGKWWEVFqps9/LkrrkWCgx1SLFI/aZ+Ph7acgjisfNIg1R8yFk+8kUJps6YRIMPy4K9dDqYfkOQnyKdivBUtCyYaFTOv6WrHuBYJZQ9evk8RjBnRtRKpSRrKBchfziTqE9F+rNghowCk50Hizpwvw2lTXzwDAve7P7y6Yqgb4ewaysErvP+LmaTbwtLkhdn2XBATz6fWH8IcibzzhChKUwiwrjWKrHjLaQ0keEE66NJLn21q7m40Oie8vtoQ2gjyMTT5RtOSadd1iullsyy5hN4Zlo/nFNrze+jF40LjOi5wjAMxuGsY9nVEhEK8lLVuOzMJlx1ah1UMn9rtdq3z4mr5aOfoQoAMMnkXhtFTK7CfXBNzsqQ+KG0txnc9gkHRtH0+IJDyx2fc1Kd4uOFrX/361jiOi8WPywS/D8eA//PhRDWfOTX8eXg/IgSEgvPSEDiI8x4MlES8Y2n606WD3d4QUA1Y3XOU8mUOFAqXMVHoySLIwAc4lNhihKlLzBAZn4uCjOTAQCmNqfZnnHpn1rUt7C8mDIs7jiyGrce+0J2c16yVXyIc4+4YnG5txdMG4T7VUdx43UTvZ76PzcMwoc3lEnWKdMzoGAYMLZjzt3KoarVt7Ug2Bhs5quBTeUo/NMCx/qMJBVe2vI8Xt+4CD0Z//yKVKJrajH54cNy3JqpV9j8o/8NdsHC+RYfbASrQAMkPnwSbI9gnorIJSSeLjtPP0E3/rGxEm2MdaZfbgB2LQVeyLW4WZDMrBIHy0OX1E1Y/03A31GeLgcAGGudfiyu9TXEU0q1daGv6ir3fFMLzoErP1WNf45W4Z9DBEzKlB/QxOlMbs9uQnp+HibOnAZtbp7s/nZYhnHzc7G3SSlqwxdbj/vqRtB54YhVfOzPKJXUvwGAYn0tsk0tYP/yrF/HEutLfywfG3OHYHmvSyEoOj/tZvEnP0qEy3oE1Lu1a9di7dq1qLX9eIqLi3H99ddjxAhrcSeTyYRly5Zh48aNMJvNGDZsGObOnYuMjIygNzxcBPvFVPyWcO8ANV48YEKv9ioAwU+hTEQProG2I9Ms2N6i8JrpMlFZV+F8y5arneEa3lig4dGgcncu3Xv4NIb2DU0hP31DI+B9bBVhba+Kt771ikOEXbuXZHG+5U+s+BnAjV1oZYCkZgAAbh+RjVd2NuHKPOtA2b1fbwC9ce6+k/ix0ZpafW9lCwbbkmfxotDiqcOKAz7tzCIBK84wuCHXOVWm4C0w2xyKzQd2AZP7efp62GFf+xgwGMCkuudAkUPsXGvxI3rnb4NuAQD0qrZgQuea6J/4yPb7Bg4JAYmPrKwszJo1CwUFBRAEAevWrcNzzz2H5557Dt27d8c777yD7du34/7774dOp8PSpUuxZMkSLFy4MFTtDxvB8vmwvwFnGZuQn1IIwIQONhL+3EQ4EVxMHzOLWWzfH7z7Kl5wrRsiNy3tGimRkiJfg2lPQ+gSVL3Sf2bA39Errf4CP4sq8bqavqe0HsAXunz0bj2NnpnhSUrF3r8QMLSDybRm9bx0UD6G9cxCbrKLA6zoTbxqx04MLrwAgPOFSslzYDSBZ/G8edIAXGW0IFXj/FtYRKrsq4yhmGsRZKfgIgGjVAGp/udfYW3TSALD+GX5sPNTWr8uiA8entJDXHvie1w17zdgVJEddwJ6rx89ejRGjhyJgoICFBYW4qabboJWq8WRI0eg1+vx/fffY86cORg8eDBKS0sxb948HDp0CIcPh6/aZLCxO4T5E+3C8zxMHd7nJ+0hXKwgQGVzRKpOck/lS8QXrrMurMr68KrWZrnvnMDUfPiuZFmpcR+AXS0f6vR02WMdRho6zJFJzy3GLjp29TnfbZvrFFL362bgP5sWY+GO18D+zj06KhQwA4aBGSmtttwtRe32dxaLPqGl2fHZbPMvYAUe6GT9ErHwAACzS1G5D/eErxx9KFDYppEsfuTfsLNF16vT5+Nl/J26GeqxdOOTmJ3eiMyM0IWh+0unJ5V4nsemTZtgNBpRVlaG8vJyWCwWDBkyxLFPUVERcnJycPjwYZSVlckex2w2wyy6IAzDICkpyfE5mNiPF8hxxfv6+t6T723AYUsKXp9ajJRueRAEAQaOh05UP8FuoVQIPJQq55//1M7d6DFimN/tCqTtkc5kFy6iur8u/qbtrAqA9S2Ib2uBIlV+APVGVPe3k7ypzwdEfwqlTuf2G3R9AVZnZgEi947S1tNoUqehQZOGw0dOYdggz8W/wkGjOhUMw2BqsRqvlEu3KVlW2r/sXOS/4hRg0XSNxdk9+QO7IBzsCXbAMBgM1hcuk0INRqPpUls9ffejffW4ZURkpgmC8bdXQAAHgLdYZMcUT+fgBf9SuLtifcmVCjqlYEH2LbeDGX5uVNxPAYuPkydPYsGCBTCbzdBqtfjzn/+M4uJiVFRUQKlUIjk5WbJ/eno6mpqaPB5v9erVWLlypWO5pKQEzz77LHJz3eP7g0V+fr7f+7IWAKgDwKCgwD2zn5gdbC7AAns278a1d9yChz76Fd9VtGDFnNEoybPOD1bWWc3KCoFHbn4+AGuY4Mmjp3HO1Cmd6I1vAulvPBCV/RVYAM6iZ3xSOgBrGGG2SoEkH/eWN6Kyv52kySWlVG5hodvvrqVD+vaYV1ICVDvXXXXqJ+zsfT5+RBoOnWzAlIulb/XB4YDfez48Mt1qLc6uBMqbJdsKCwvBpviuWhsN1/hUkxmANX9KRXIBVF99jIxepVh20hn1UdC9BxhF16MobhnTA//delKyztfzN1gcqWoK+nmV2AkjgJTkFNnjSa+v897KTEtDcmrgVgqN+hBc7a1KnkPRtbMDPlaoCFh8FBYW4vnnn4der8cvv/yCV155BU888USnGzB9+nRMmzbNsWxXZLW1teC6kGRFDoZhkJ+fj+rqar9rPzTUWc19AoCqqiq/vmMwGFBVVYXvKqze6m9/8DXunDEeAFC7chmQMhGswKO2xfmj/ewsg/MrjoORMTN3ls70N5aJ5v6ePSst9d7S4oxkqK2tg0ob+AMmmvvbWVpV0peXFmOH5HfHMAxSs3Ik+xhcnOsuOLsT5tEX4Ec9sK1G7/fvNhRcdvZXDB56C6qqqtDa7h6aWd3QAKbVc8bMaLrGzU3OxG1fFp8PU5UK3318Gpef3QrkjQEAVJ896+nrfmHv74AM9zfzcF3H/QdOOz5Pa92HqqquZ5llbdeuvq4OKS73s7fru3XTNvQbIj9r4I3WtnYA0loyKp0u5H9DpVLpt+EgYPGhVCodKq20tBTHjh3DmjVrMG7cOHAch/b2don1o7m52Wu0i0qlgkol77wTqh+bIAh+H1scpSD3nVajBclqFuJNh49XY5Logaioq3J813L6BNDfavnITHKaMQ+ml2DrIw9jzLPPB9od330IoL/xQDT21zXEWuyAyjNMl9objf3tLHXaDMkyq1C69U3hYjK2Vn51hg0y19+Kwd2LgR3AETYTBqMJWj+SZYWCS8b2cbRfNp21UuXXtYuGa+zqn/JdwVgAwJc24dGtozFobeyd5f4SFq7+i90lRnfTBOW8CpsVwmK2yB7P0/X9v90WfDw48PO7pocHgDsuHRTxe0hMlwNJeZ6H2WxGaWkpFAoF9uzZ49hWWVmJuro6j/4esYDdEiPIxNxWNHZg9sojeOKrozCJMuKtKR4P4ZQzNl0hiqe22P7krMAj3aWy46Khv4uqm4MIHt+d9JypUQhW7v44wOTiaOiaSh1wdzhVaZxTNcNaj4O97Frkl/VBjrEZHKvAgf3hzxNhR82K5/djO6eLazl5V2q08qXbO0NSBLNvil84lUGqvaSwHdOXNd/1+e+rSKHl2EFwh/a6rxeFP/doq8IHV+SjrJvv6b1wEtAVXr58Ofbv34+zZ8/i5MmTjuUJEyZAp9Nh8uTJWLZsGfbu3Yvy8nK8+uqrKCsri2nx4Y2vt1UAAHY2WNyyKXIiE2uHwSk+WhRWRW9QaqQ5mu0YI5NOmAgtHx6RmtwZ0UOGtIdnFDL+A65jICOqt5FmtvpUsUolBtv8E/Ycq0GwSTHr3daVtFW6rRMXZnPNZhpruFqcQs1tvaTns7Q2e9gzuIgjRVRMcMWHr8yjvIzYqNebUVPfAmH3VghGZy4U3mLBX9aewL3r6sG1SP82dvFxhf4gXpx7AZKiMNdWQNMuzc3NeOWVV9DY2AidToeePXtiwYIFGDp0KABgzpw5YBgGS5YsAcdxjiRjMY3oBycIgsRLWNHWDLtrvmtIH2fQAzbnua+KxuFO23c/KbT6flQn5YBhGMwt5vDGadFlaG0BtPJ5C4j4YXifbsABq0MdZTn1jJzlw20QFA3wfdrOOD4Pzk3Cjy3AXs+ZwTsNL2PFeGhKP9yxXnoyjdYZespEOJ11V2HDLJ4uP6cv3qpwpmkwtBmQ0omosEARl0JQBskSrbSJD3MnxMdvVx8DANxxaC2mDNoF5gbrmGo0cziS1hMAUFNdh6I059/GbohXQQAbBAfgUBCQ+Ljrrru8bler1Zg7d27sCw4R4sJPAs9LPLnFRa+MOzZDnPLQ1G4AkOE8kNkEqDU4kSz1Wh83ph/eOH3MeY6WJjC5zn3OHKlAkopBVq+eXe8METUk65xv60KQTLuxjtyUo8JD+m0JuhQ8vvM/2JnVF1NPbwBwDwBgyICewOYOHFVkQa83QqfrXA4KOVwz0yp5DjmZKQCk4kMlER+xLTLDPW2kUbJYPasfpi8/BABo03cgHNkpeJG/ngrByROjsR3HV70hS5vnlPr/7ncd9rcex/2CAJZhUCOueeMynWMXUK5TlNFEbP8aIowkbW5drWSbweQyt8fL38SutQ2O1jgfXvVNbZi3pQO3bTB0qbwyEX2IfffIz8eKPV+EGIUf9S0YhsHQpqP4TfmXEv+qbr17Is/YBAurwMG9R4LaVlfLh4VhoVC7Z4xUJ2lF+0gHgsXGjUFtU6jxNZBNrN4WknOmcdYpS0P5UQheBudgIXYOD5blQ22zfJg47+JD6JDWWynQS5Or/ZxUgu+2HgUgFUkmozT83H4aBRO9zxYSHz4Q/95c63OoRdasRk76p2w+IY1R91RZzFV8fFXtPMe+486wNaMhtCXCifAifnsXoqT6aqRpaXafH1HITLt4RWRFYhgGgxXWY+6uqPX0jU7Bu6SuFhgWULpH1KhEv28jL+3LwJLI5+4ICB8v0XdePSYkp7U7fZrXfAz+odBb1cW/R6VbbuLOobH5jhgt3sUA7zLGsIL7S+s/j1igbzegtsopTPY3u0TT2SOsyPIRw4gHCZf7UCm6rh9yRZJtf9FKUykLHgr9uGav66N0Opw21ThvrhON5IgaT4ivOm8hqxYANDe757vwd76aOe9C6/+Tp0nWD+5m9Z/a29b5CqFyyBYElHOOzXCmz+9w9Sz2YA2NVsxyDgkidH1CE1hg95ewMCxgDP1LWG2l86VPEaRpFzXjtHx4s3S6itozyd1k97t/xS48td/53PjPGanVTXBMu3SquWGBxIcvvFw8teivZxC8/yntVQbt5arvOf6pY9t71/d1fNaKbvYWs0iBx9iDivCOxKIWQLGpeKa5zX1g8ZUt8yFYwwyZW+4G++fFYGbcJtk+2JZa/Zg6Cyaj97pLgcAz7u1iGAZXnvrJsVzcXgNG7fT5MIp+wpOqfwX8qTwaRWSnRsYR3hEpEiaH3W/qRX59HizWgaJmrX0w7t8F/r7ZECpPyu7nKj48UaXO8LrdYfmI4sKVJD58wIj/RILrtItz28RW78XzLDZTXqZgfcAWjBrp2JaiUWCkxaq2xS8XWpHJLVkRvXN3ROCIHwkCWT4AACY5EeZjwEmdeAkAgFGpwPQbYq04KiItKwOAVSxYguQ3xXt5c51z7AvH598e/UyyrUM0jt129H/uptQoJyNJicUX98ALl/dy23ZHavDDme0obVYDTkbwhQKtxnkPKYI27WLtw6riiXiu19Uwffim7H7+POVHNRyU/67ZKa7ttygTxaYPEh8B4HpjqEUF44SUNK/ftcd3G21/co2Lr4f9HhE/2Ex68VQLiY9Ypru5UbLM2MpsA4DA+V/pMp6xyPm+eIgQ+eO4Alw/KBuDirz/7iTRakFyHhQfJs0knSpiRb/TogcelWwb2M2Z+TmV04MZd3FQ2hNOBnfTodQl++gnN5Vh6lUTQ3ZO+1PWEqZomyI4c7gkq4MzXWcPD2/QpOOX3KHYUC9/L/pj+ahTewg3Fk1JOX0+AmxoGAnuRGgcIr14LjeM6CnUyitciwhKsNhMvibb9Iw2Q3oDnWCs2efeas7CxVWnwRYUo+P4MaBHsXUHH/OtRHQjzpQ4tnYvgP4Q7A+kxjZkefheImFPjJRpakWj2vp78BSeOrk0wy8xIU7sJQTpNyROXa1wsV6wS5bh33XNaE3PQ362tLbGoG7JWLzjVXQz1APDxoJJ9S6copnR6Tx+bWZxjs4Q8hBie8QGx4Z+uBIqjoCrrwNy8nDXoZVgHvpjUI7rmiJlf3oJJsud3w+xcCJFvtCdxWx2DOjkcBpnuFpJxfU5WgXvPwzzOy9DEAR02NJHa7OyJdtrGeuDSq9MwtfvWf1BOhROJyKKxoxtLLY3mstPb8D9BdKojsXl7iGaiYh9UC/lGvHXzEq8UNz1CBXxtGmwLB+8SMQoXKIRmLQM5Jf2RF8X4WFnQHMFskytgB8hxNHM/Zf2w73n5uOP04aG/Fwqe2ryEFs+eEHAxn+/6RC+yjHjweiCk1nEVZ99U3iu7H5245+SD3yKcOOPvzo+O6ZdSHzEMp4vnniKxJf4mFsyB/WfrnTkB1BrPQ84/yq4CKbGBhjzip0rSX3ENHaT8fkj+0A781bJtgZN6LM2xgL2aRcFI+CcqZNROnFC1w8agnwqYvGh6uSceqwnHEtWKzC5dwZ0qtD7YTiKsoXY52PrmTY8N3gOjqV1t543iJfI32RfduscK3Ov3lz+pdfvLtE7E1Ha00KQ+IhhpA470huiQ/TS0+ajAhDPKPBorTNsSuvjR7tj7ToYQvDWRkQGznYtVakpUf1AiCRcjbXct0LvucR8oLDiQT5IPyFeFHmmzAxwwsyeiGzwqOA0JgFQOiwfwRcfQlM9BFvNmMN10nQGNaeqg3Ye15IA3Qz1svvZ83wwLjfrObV7cPkffuf3+cjyEQdIpYf0hjCIxMf+pEKfxzqjs6ZfV1vMUCqllpI/7X9Psmxpa0M143RQI8tHbGO0PTjVwXydijMsh/cBAJRc8EJixT9gPkjRJZIMmMrABkR28b/Bzl/gyEtC+MYZahvc345gNuGt11bigxffhsBboOOl991BLnihxayLhaxZLT+dY79FWZd7dXPuEGhT/Z8CigWfj9ieeAwHEvUhFQDb2zs3V59pagGUxZJ1va67HjjgXH5W65otkMRHLGNirD81TYxXNg0lZ3S5ANwfvF1B8vANksOpeNpFGeC0C5ORDQzP9r0j4cAhPoJs+aita8anPaxROtc3N+P9Ay0Qv48LQcyR4Trt0qHQ4EhVM7plpUDsPmoXtq6WDyCwey0WLB8kPnwgDdWTbtvf0TnxkWVscctfwBb1AA54jpUPlqc+ERlMNk99NYkPj3xdNA4AsDVnYNCOKUljHyQBL7Z85OiUONIYREsN4YY9z8dr/a5HkzoFNwbpuGKXzn/8cAxmpEq2T9cGLyW/nNHmz99bpxl/vpkBbMLKPu0i5/PBMAx04KD3MmwLFgsYhcLp80F5PuKEII3/maYWN293wUcypWA9OInwY+EFx3y1JkAzfSKiV8pHinQGsc9HsKoHCxb7AGHB70fmYkxRMh6ZVOzjW0RnUYqefe+XTAnaccUvdOsNUuFx5amfMOy6q4N2rnbB8+/+htc3QDhuTVJp17Wsy/M+w2gtqPfeTQOxqneNLVzfyuwO52c0W/MJxYLlg8SHLzw4nJp9FAjyxpG07m6WD8FXDYsYy4ZIODGKkmepZUrEE6GFsf12gvUTsocEs4KATJ0KD0/qjtFF4Sj2npgoQvTi5ekZ/tzhZZj70O/BZAZveszgRXxU6nJh3LoBgPMl03XqccHet63rWRaKcyfiN7Mvg0qw4NqUJlx0zSWO/fS11npg5PMRB0hdPpw36+kWo/vOftKoTnOL40pN8jGFQ4aPmMUkioQih1Pf9G45BaB/0I7HwPrzCVqorWAXH7zHDKxE8FCGTHzIq9G+t/wm6OfSexEfAGDILYYGAG9x9/mYWL0Nfe+5T7J/95xUfDBrIJQsA73ZGfkw61fg3zjiSArvb4hvJKBfjk/kL15XLB+DmsrdLB+ZSSr8RVfh8TsUahu72KuBKnkOTJL7lEIy33khG0/kGRoAALOr1gX1uI409kHO88EKQnTnr44TFO0tPvcROA6CObAyBZ7EB1PUI6Dj+IPBR+pSPW8dih2iQRAwt5seReYmzL5+Iphefd2+Y3dA1bi80Oxe/ysstvO5Vk2PJkh8+EBSfVSkRmXrULgw7dTPeO7cNJQp9ZL1s8u/lE0yNG76FFzHnJI/GGmPmMXCi0ypSc7wvXOSraJjgOms7PcSDaXK5pQ76bKgHtf+Fhksp217ZmMWQlSbteMFX9MugiBg77NPo/aJByAEUDzQxHl4hiuDPyHA+RAfBlvtL/s9ykDAlRePxKu3nou8nt79iVwFRnJurqOKejQbWqO4aVGI6DdgsalshZdS95MvOQf9ehfioSuHSNan3+w5WYwhPUf+1OTzEbPwtsJxrCAAWqf4GJtmfVBSIJMV+5+BDVHp9OBFu8jPyxOhQVngPYfSlv2n8XCvGbh9wJ2oOeN/dV2P1mtf/nedIA3erTIGsy2c2JZoj+U7f29xCiUsrVZrEVtxpNPHCTUkPnwhNX044E3Wt1alS22HTKPTRMgo7eGV0j+zOsWzcxqT5iHVNg1QMQtvsFq+WPCA1jntwtruDxIfQHNtA86y1qR6bJAjghxhi0HLcGoTH/SjDAtml2FKsEifufs2bXd8vmO9tW7Sxo+/wIm33/A61Wb28MNjQiB+f6esAGCdWvyt5ozbdoPNCnN8pzXRXqq5PaDjXyNKFmKxCI5yDoqzlZ1obXgg8eEDifYQ3ci8wZqKV+VSAOjK2i2Oz6zCntVSahZTl/bxeL7rRnWX30BvWTELbzMFs4IgmW6zD7IkPoD/+/wgLLaHfvDrnlj/wMHOcEqWj/Cw2eCSadQszauSyUqtCodO1+PZjt64RzUeDVs2QxAECKJy83YMMtMuVzOnu95gGfK6F2DVjw/iX5ufwfkXjXXbrrcNI83drPVZStsCEw2zLihzfOZ4HiZbAVN1ieexJtKQ+PCJOEmRE37/TgDS6oN/3L8c40WlHuziQ+UiPrzlesjWqfDO5Ew8mVeDe8bmOs9NA1TM4nxTlj7sFLZcLzSEAWc0zh8OG2Szt+PXF7QMp56zUBLBZ0KKtOaKYJI6aPPdpD4RL61x5r34x5az2PDvt7DiuX+Br3P6Vh2o1eOl/e6O3r+ddXEwmuzO8HPB3PZHsE++CoXaPbJRb4uIswfGqQuLAjq8RsliLG/tn4UXoNda85boBg7x9rWIQuLDB1KHMufDRp+VDwDQWZw3cDLXAe3VNzj3tr3BuYY7+UpymVHQDcMumYiL+mYjjbOa7CnaJXbhPVSqJMuHFYuLkyATZC85h8NpkI5nr2Ytl4WSCD59tNJplg6D1PJhcbkOp5OdBTx3Z5Xh+dRxWF46Bcc3bHKsf3+H/74hwYBhWbDjJoMpKIZG5e7QarD5n9hcP6DshCOzfVxpq6yCwWb5SFJFr0M0iY8AEN/jHbYUKakK50pdrxKoU9Mcy6xC3mu6cx7y9KCLVTw5KNrFh2eX5cRg3wlphU9VkI8fslBbslmFBY3LKNWil06hbNNr/TqOSXAeqLnRPXx38YG3Am9cJ9CpFXjasB6Dmo451h3mkrDhZAvqeevdr2QDv1eVtmHl3cwxqFNZxyGdtnMlQMIBiQ+fyDucmmyhtlpRhYDkcydAo1aguL0GWcYm5OdnBq0VVNsldrGcKgcgJz6sD5pEv7StJqn8StYGV37Yf8EhyfNBhBxtmjT1ucno9PGobDbgoNm/dPwms/NZrbQ4rSc3CMex+uoiDF70bBdb6j8D585F/6IMx/ImRQGe+7kS63nrVHs5n+zhm56Rs6gnJQevMm+wIfHhA6mRwvmw4WwPoBTBjFHpPHpoLCgeOgisQol/XFKEf0/IgCpF+qPp1Pllzk3EFs1ffwEAqNNKxahj2iXsLYoujCbptEuSJti2DyvB0gpCSxMAgPUSZk8ED022NM25RZRMrLqqwe/jGDWigVgUMaNiATYIz+pAuaLEs8AoNwb+G7BHuIjRpZD4iF0Y+eGfs83RqSDg0WkD8dJ1Ax2OpaqSPlD3DU56aIdTG71lxSyLh9wmu94e0scn+M9QLZp4GlO3D7rewfXQZ4P8G+J+Wis9LhFSNBrp1MHJ085qswu3OadPLq3dDm8YW9scn9tEk3vNQZ/o84+socMws3mH7LZuhnrZ9d74mXfPEaVJCdyCEi4S+6nnB1J/U3umRAvMNo93e7nnUGc6JO0Rf7C2zIQ8wySMQ7E17FEaZdDWYX2THdB8HAtunxLCUNvg/I3LFdZcPGd0eUE5HuEdjVoa/fRmtQZ//qoCK3efBS+yDZcJzV6PYxSZGNtEZc3ahMiUOGMYBimDh8luu+Hsxi4fX8sZwWqDVyE62FBhuQAQABjb9XjtzTU4pO0G6JxOPqGmtroWpeE5FREm7FFQAhirukyAVN3XLD8kWX5zWne8dtQ6KhxILwGjC/6bGmPXHEESH+9nnxuU4xD+oVFLLRNN6jQ01XfgSL3U8bTP2OFAhefjdNhSnPOCgA44BY2nZGPhgJf5zV9a+QtGZAR+rExzGxpVzgSWOt4YAiEfPKK3ZVGD6ObgBXz64x78kDUYlTqrY5CKCc+N+1RjIfSNTWE5FxEe7JaPkykFQUuAFc0YzO59/PSrrSE/ryPUNkg/1Ys0jcE5EOEX/mS8fW7bS1D38T7VbbQVb2vusIBjne/d44wnu9bALmCRKVzar7kC7K33BHysh0dKM2ebmdCUKQgWJD584CpMj3ZIjUVJTGCDRoHC5HsnEU1K55tgw+noTZVLBI54qu7Mzr1e9owPqlvds0yq6qpCfl67+OC7UC9DjNbmEHuF4XBQjkd4R+EhZYGYsj8/BI1MWOldY7thcpLVL8Ros3x8vMeZbOzZbS/jvMmRs2TJFShVlZaByciW2ds7fYb2x5VVzumaVlX0+nsAJD58I3E4FQCXBxgno1zleHxydwztpsPjV3TeEdVs9r9iIxF9PA2pQ5xY2Jp3ht4CEGnqmg1u63Scc91tjb+E5LzBjhgz2QYx17IJRGjwa5BKz0RGqntkx5S+mUhVWJ/ZHbY8H58fcTqp9ntsEdjSfsFoZqeQq3arSO185M1Vc290fO7RUdfp44QDEh8+YF0Ky1U2Sgv+cI3+hXqNKEjGwot7ID+180lfXEMSiehHEASk2IpEpQwZLtnGiIZF076dYWxVZBDnZ7DzqzLf8Xn0qAEhOW+wp12Mtkg3TYBWT6Jz+OUKxbBQsgwGpblv0timN40yAz2THrxcTJ2hqKd7xV5FF8J+8zJ06KWyWtcXTu3b6eOEAxIfPmBcLB+nkvMl25Vh8vkAAKOZ8grEHJzZWeSpUFqDgmGd9xYvE6Mfb/Bmd/GxP8PpRp2cFJpsjIwj0jZIlg9bQTK1TJpsIvi4lqfwsBMAYNG0fphkOCbZpLW5PhgRfT4QE0vTkWVskqxTytR+CYQXZw7Fpzf3R0ZBN987R5D4f+IFE87d8qBODt+8WgeJj5iD6+iASWF9mOiSNJJtjIccMvGKhXMXH2KSXf4+wSZYlo8fOet8vILER1hg/bJ8KGz7MriphwJj6/biqfL3AQAptpDERkYD4dTxUDWzU7AMgycukVooFH51OPYh8REA7/7i7hV96ejwBcB2yJSAJqIbQ51z3lXn8mYvmdFLgDDbowe8P/hVBQUhOa+zsFxwJd5OZPneiegyfv00RAN2t4svwV8n9cTAP/4JAJCms1oe9QIL/oP/hKKJXSIjX5ovRhnkworRSmL0Mkh8q3efi8saEJp5aju35jkd8owkPmKOde9+7PisdHmjkVo+4v+n+LnguUy4greASQvN/LvCHu3CBddyeElqu++diC7j17SLaNqSYRVgho0BYyvyqdFZE21ZC8s5j8VESXh7WZE0ssX1ORGvxP8TLwh4u0kZjX8VFTvLNRcPR5G5CQCJj1jDYOLwRumVHreLfT5kfOESigV73gzZsZW23685SOKjm8WapjtTTY/PcODXT8PLgK1OsooPIxQSM8pYNjrytbBKJeamOVPGK8jyQdiJ5LjAMAz6qaz5EYwG9zwJRPRS0+L9eomjXVqiPCY/1GimzQjZsZW20n1ckMSHffKGTZBBItIoWAa/HZmHm4a61y6xY6+TJIfGVtnVyCokyfzUYcgx4zcKZ/sVXvoST9Cvpwv0QpvvnYKAxuaI16F3z5NARC+sD7OueNrlb4Nuwa9nWkPdpIgyReO5WJZKFbriXk7xERzLod1xlVEkxiARDVw9IAs3DnEXH0Maj2BO5Q9ev5tkq+zaoMnArblOkXvJoOiJBllb6xyKFeGq2RFhSHz4hfVpo+Sl0S5X4ExYzq60pRf+SB25ZDhE4Fh8vGkzLqbiZ384ASFIWTijEY0t5jXD5C6yVCGMHFHafr+cJTh5chyWjwR5Q41mFv72Qky/b67XfVJ1ziiqNpUzEdngfj1C1q5AqVQ4/QmVUVyPJZgkRi+7iD1PgLgeAACEK8HhAYPzrbCpNvBSy0RkMPkKjXZxpDMxSqApfq8vbyvgNalxH2ZWfCPZplKFbiBX2S0fMqms5RAEAf/5tQZfHpb3CbAfhSXLR8RhdClglN6tZslq+evEdjGfRjC5jHNGUipIfBC+CJf4MPLOE327h+q7xAripHB/LXafopPz4n/99U/QsOr9kLYrUtgdppPy83FjVjvG1+1xbMtgQ5fDxp4I0OzntMvJZhO+ONSIf22tQZPB3Voi2Hx1GD8KnhHB5Q8HPnR8vlLnn8Oox+gRH6IlnIxRNjs+ZysSI5M1iQ8/YDzkBwiXv1mJQu/4rGnzL507EXlMtqR0JW1ncM7E0W7bC9Pck2p9UTwBtxlGQKivddsW69RZrIN1qloBxV0P4YF7Z+DZbS/hmW0vI6Vn6Ezg9mkXs5+WD05UYv217w65WbDsW705ORKh4aK7f4dXf3kG8w+uwJy8LvrAhdDPKFAGX3EZhjccwo3Hv4aa956ML14g8dEFMpNDm5HRzq2TBzk+mzoS48aMB8y2QUvNy7/Va5Sef37GxugIAwwmFRZryGOpKLCn3+NPof9DC0KW4wNwWj78nXbhRalQf2lW4PO1W6TbyfIRMZj8YhRcPR0X5SugPGdC1w4WReJRnZWFR3cvxcwT3wF5oUm2F21QfuBOcHP5lzCzSgycfkFYzpedk47Lme34UiiAIX79EeOOtn17APSGgg/cjCoo4+unKTTUocP2rpPavbtjPZOWAaRlhPTcKpvV3W/xwUstnUcrmwAAFl5AY4fzWpLPR2RgL74auPjqLh3jtsZfAN2cILUoOLAPPgO0NoPJL/a9cxwQX0+4EKGEAPHwceHAQmQnsWAGDA1bGxQsAIv0rYyIbl4xlwCMtHiaKzN0dfhI7x5CKKjDY1ULFxVbtkKvLAEAaFOSwnpupUN8+Pfbcc2E2mFzNF/06S5s12sBpTViglHQ4zNWuLGAwwdV1us18/Q6XP3g7yWh7tEA03dgpJsQVmjaxQ80kD6MlCPPATt1RlhvXrtzIk/aI+rhP3kXlnnXQ/CjUq2Wlb+gQkRT2wWf7zqc0yqaMKePdogPP8OYBc4kWbZfxe16aTZjnYcoCiL6uHBMmeOzApaoEx6JCIkPP9AIUvHhKXQrlNidWy1k+Yh6Vuytw2sl01CgtxaVu0x/2OO+nh6B8WbhEotmbVpKWM9td63xN8cYb5b6VSkgQHC5HmmmNqTrosdhkfBOXorzWjWo0iLYEsJOQHbD1atXY8uWLThz5gzUajXKysowe/ZsFBYWOvYxmUxYtmwZNm7cCLPZjGHDhmHu3LnIyMgIdtvDhtrF8qGKgKMZazPx8lTfJWoxcxxOnKzF+yVTJOv7ZHueZvD4AhZnJi6Gsw7oo1uPQZXRP6znVjIABMDs599UMEt9dBh9O1786YRk3ciGg1Aou4OIDcRh7W0poXNuJvwnIMvH/v37cdlll2Hx4sV4+OGHYbFYsGjRInR0OGtYvPPOO9i2bRvuv/9+PPHEE2hsbMSSJUuC3vBw4jrtgggkgWFtGSAtXGLEgMcij/93A/60qdltvbeoFsaD7YMPcvn3SGOy+Vv0SQu/n4Q9H4+/1qT6dum0yy9ZA/DDaWmdnpH1hwDy+Ygp5pYwyOA7MPPi4ZFuCoEAxceCBQswadIkdO/eHb169cL8+fNRV1eH8vJyAIBer8f333+POXPmYPDgwSgtLcW8efNw6NAhHD7s2fQc7ajhYm2IQIiW0haT7q/HPhE+2kwWfLD7LPaq5WtFpKs9zy97cn+IkmrfQcNkszqow5WZT4T9pdffmawXDph87pNrbATiLCIp3rlyXD+8PXsYepUWRbopBLoY7aLXW5NfpaRY53DLy8thsVgwZMgQxz5FRUXIycnB4cOHUVZW5nYMs9kMs2iOlWEYJNlKIAfbKch+vECPKxYfV51aB0YRXmdTANBpreJD3673+9yd7W+sEqn+vrK5GhtPei4Kl6X23CbPTRV89iNWrm/D3n1Yx+cCDKBWKbrU3s702W5d4gP8njc0KgWY1PSQ/+1j5RoHi1D3N9r+jol2fcV0WnzwPI+3334b/fr1Q48e1uyETU1NUCqVSE6WlgdPT09HU1OT7HFWr16NlStXOpZLSkrw7LPPIjc3t7NN80l+fn5A+6tFqUyHNxxGQd9+YMKcHS+X3Q4AaFcmIT8/P6CbNdD+xjrh7u/Gkwe8bi/uaERBgXziIF1SEuA+U4OszCzku3xHEOQFSbRf3zlvboagtjr5pSVpPP4tAiGQPmvUasAAKJUqP8/t/XoCQO5v75H4uoWaaL/GwYb6G/90WnwsXboUp06dwpNPPtmlBkyfPh3Tpk1zLNsfrrW1teCC7N/AMAzy8/NRXV3t5r3uDcFiAWwzLcpR56G6ri6o7fIH1tAKIAntyiRUnTnjVznvzvY3VonW/jIDh6Kqqkp2m7GjA0Cy2/ofj9ZgYrqz0uWpZiMeWnsCg/KS8NeJVkfHaO2vK41qZ3RB8+GDHv8W/tCZPpvN1mkUk9ncpXOLaeEsQTuWN2LlGgcL6m9so1Qq/TYcdEp8LF26FNu3b8cTTzyB7Oxsx/qMjAxwHIf29naJ9aO5udljtItKpYLKgxUhVBdDENxD57zBMs59FRptRG4SnS0fRLsyCYKFA1gWte3W6arcZO9WmED7G+uEu79lSgMOc14iWnr1Dbg9J45UQBjZ27G8+VQrWo0W/HKqDRaLBR2nTuHwD+tw0a2zITCKmLm+JZnB+f0Eco3tfjWCIH2mnNh7EBs378fVU8+Dzk9rzDlMPaBWo2jA6LD+zek3HN8kWn+BAB1OBUHA0qVLsWXLFjz66KPIy8uTbC8tLYVCocCePc5qlZWVlairq5P194gVeFGyKFYdmdh+e5h6i0qH99cdxI6DpzD3k2OY+8kxmExU7yVSCIKA7mp3C10mb8BQSx3+0afd6/c9TZ9ZXGq7KPVOn5JTH32ARWsO4DHVWLz90tuBNzqCDJ55XdjPKfb5EHPPLuAD7UAsX/Wz38e6f2wu/nr9mIScoyeIYBKQ5WPp0qVYv349HnzwQSQlJTn8OHQ6HdRqNXQ6HSZPnoxly5YhJSUFOp0Ob775JsrKymJafPzKOM1IHcrwpoa2k2xL09imSsaHNQBqnINa69EjOFHdiA37zmDuVaOR1NNzOm8ieAiCgO2v/guV6AVkpEq2jeOr8fs5l/s8hqchrKe5XrKsFNWH+auhH9rSrJbFN7LHYZoQvflQxW9zM1KbwGjDm+MDEFs+5N8sd6vyZNcv3v4KFoycL1mn1mll9yUIIjACEh9r164FADz++OOS9fPmzcOkSZMAAHPmzAHDMFiyZAk4jnMkGYsXKvUWjIzAeTXZOcAJ+W1GvQFPVGUDWdn4dr0JnxRb/PIJIbpGS4cZT2ZcKLtNwflnjfIUOq21SMM9xfld2lRSH5FdOw9j+PDoFPcHj591fC4xN0SkDY5QWw8S7URyPn4904Z+OUlI1SjAChbwjAL5184EKlyOpY3MywdBxBsBiY8VK1b43EetVmPu3LlxJTjE9Kg+AkB+wAklTEkZsP2o7LaG998GRG9oRw+fQN8BZP0INXqjZ4do1uKfs7TRpYiZHUGllixbzBw8/VzbD+wFolR8VO7aDcDqIJvP+s6fEQrsUyTeEpwu/PE0eumAF67pB56xCndlcU88vucrPJ4qql6tIfFBEMGAarv4wSWVmx2fB118gZc9Q4fCSzGuHVn9JMuff7nZw55EMFF7yULqbypvoyhd/lsbnkDflpMAAKGXVEx4y2zLtLf5da5IYLFZG7KMTSidcmlE2mB/yAk+ssZW6AGL6LopFAqMuPP3eDjjtHMnTXxVGyaISEHiww9GDe7l+MwWRCY7HuvFwW17tnQefV23ERD8NPsTnUfg5a0WgP9FzEyiMu/pLA+1yvrWzbukOPWaVr/D4N/JIgCntZaf74s2MEm6iLTBbvnwRw6am5scn5W2Gk6jeuVg3sGP8Ny2l8AoqZgcQQQDEh9+0HvsCOeCJjIOZ96qkJenFruvbKgNXWMIAABv8Sw+zH5GzXWIxAf78gdgbPeXawFBixc1E83+PXZDgoKJXBghYzu3IPh2y21au8bxWWETH0xpGS6u3oo+6shMGxFEPELFCfwgLzsNLww4gRSNImJvPt4sH277CjyMbe1Iki81gq2n27DpVAt+PyYfWi9Fz6KRqlYT2kwW9PVSKTZcCF7q7GQIRr+OYeLheAVgGAYKu38C7zz2oe378GGz5zLgfGqGX+eKBJxdfESwUJ491NafFqyzOPMW2S0fTJIO7MsfAEq1p68RBBEgJD78pHTkEN87hRBvlg9XeIbFe8dMmNvbfZvAW7BonXUOu0AwYMY4mZ2imDs/sxYxXDqlADnZ6RFti9lDfpVJLQcxY9q5fh1DLD4A53W2iITNgwe8WzZMURRoa7bwaDZakKNToeJsC87orf2IpG3Grtv9ccPJZc2OhCCM2unfwWgjM2VEEPFKbL32JjCBiA8A+OysAkL5YbfcBlt/3ub43LR7RzCaFjY40eix5fNvItgSK8/9UOG2bli+DvfddQ10vfyLNuqfKX2bVrqIj5Y2vc9jmP2YTggXC78px+9WH8O+8mrc+00l1ppyAADKCFo+WA8+H2qLu3j8SG9t7/CGQ2B07mnvCYIIDiQ+YgSGYTDOeNLrPqwg9UE4+cIz2LRiFZpbnQPYL6LcVVpzBw7tO4ZPPv4Opg7/pgkiSZvR2b9/qwZHsCVWTigzJMv/uqoUj17YPaBjTJlyLu7ObsC/xluTlNlnwTjbtMu+k42evurAFCbxYTGbwfuot7Sr3rr93R8PStZH1ufD+r8AQDDoIXAcBEGASeE+hVqtzQIAsCmep7kIgug6JD5iiNkT+0PhJcJiyplNkuU/nPMA7j2ZiQc/2gkAqG9qx3cG50NV3d6MFzecxlsdRZjx8XG0VteEpN1dpV5vhiAIaDVJ+y4c3huhFrkzC+UoSFVDGaCJSqVW4ZIp41DQ0xpFpbSNlJzNETVD6TtsxhyGn7HJzOEP/92Kp1//GgLvu03HFRmSZYXgZ/hPCLBbPniOA3/PjeAfneczGokz+LY4EQTReUh8xBBFvXvgjWv7QGGblH5h6xLJ9m5q+bfLKnUmTHo9fvvFKcl6NicPZ5KdXqk/rI78VIYrX28+gt+uPoblL7yFlvJyybb9W3dHqFXuzJzlO5W6P9gtHy0mHqaDe2HxElFjZxnbNyjn9sYXGw/hjCYLW9J6A63NsvsIZ5wpeA1KaVSYwhS5cGBHkrHWFuuK2mo0e0kQBwC702PLF4ogYg0SHzFGVrIG/72+L17JPIqSex+UbNOW9vH4vU1fuRfPalZInejqjZF7O/XEq0etg++KbuNwetVKybYqRD7ixU6wCo0pbdMTK7X9cff6Zuz6fqNk+yW6Fizo2IJntr3sWCcufBgq3j7tdBnl2uWL5Z2tOCW7HgDaVZFz2HReGgYGhRo8GGyokBdQBEGEB4p2iUGSNSokT51mXdjonFvXKFlk8AY0se6DsrGjw21dJaRvp/XajKC2M9i82n+GZFnLcxCOHoCw8Tsw198GJjklIu1K593/tp1FXH+kJikbTPU2IMO5/bYrRiFZPRbAb4D3Drp9Pxx0tLVBLuD8iwbPoaijNJGbxrDPhJ3VZuDmCYswtOEwpiJ414wgiMAhy0ccoVYpcGOhB+uF2exWrGybWpqt9efUMo+VPyOBxUdspCIpCa1LHsMHJ3nseuzhMLXKnUvki6J2itMWafruE2nOa1Sir0GyOvxBq9/uOSNZ7vAQgZPEe86qq4tgNVi7VcqejG93VhkyReqph8aCS4Qzcl8lCCJEkPiIceaf/cHxWaNS4tILR8juZzJz6FD4TpJkbPQdXREujD68Ao+08Lhl/JP4sORSPDb8jjC1yp0LM/0rIucPqazUx+OX7EGOzzeMkorF2eXWbJwqL4N+MHh5d6tkucMon+mzvYPz2J78kcOD3i5/kZsSq9qzDwCQ31GPl68fhIxa6ZTRU/uWhqVtBJGokPiIcdSjxzk+a9VKKFgWBZZWt/12CZmOz9e17PJ4vI7j5R63hRuTj7DOjzWRreSqsIU2axTB+xmxHixPVzCVOG+k1KenuN1arr6ktTJo5/eHDoO8+Gg1WcXiTUm1uL+wDQ9Wf4UXJufj8XMzUdgzMjWRAMimYFveYA1ttv+9R/YrcGy7qGoLBt5xZziaRhAJC4mPGEelcprhNRqrLXnRlD74bcceyX5bdD0dn9VDR3s8XsfZ6iC3sPOYmuWdAnO4yFdxNXZ0wGIrva5mg+eoy3LyA3uRxj3qRXGOtcKyhVWEdbps5y87Zde32rRiqk6NiReOxvl/+iNKCzIworeHPP9hQs7ycTbJms+jMsmaVEzRw5kUTsVzYHpStAtBhBISHzGOSun0GU7RWsVHTl4mrv6d1Dmzv+ms4/Plwzy/hRoiGJXgivGzD2XXl7Ly0RbhZPvxBsdndRCL+LEe8mEkwV18dAweCwA4lloMmENT9MwsU7/m3Z6X4g9vbsC6z76TrG8VrI+T1JToiUIC/KuLJBYo9dmBJYojCCJwSHzEOB2iqhnZKVKfjkcmOavd2ounXmyqQKrO3fcjl7c6ERo9hFGGE4G3gH/vX/ixTn7QUERBNvFkkbVDPSB42VYZD2nI04sK3NbtrHMKDuGdf0IwBT9LrdmD0+9JTTb+3ioVsfYYmNTU6EpL7k8UtLgy8FYtiQ+CCDUkPmIcg8gpU5UnHaDGFKdigMX6hm6yvZUqWQYsy+LaE99jcONRXJBpweKLe0Bne/bq2yMfgihs34zvD9RgZa+LZberokB8KDjrQK/kOSgKgzhYeZg96TXQPZGYSuRrsvaUAfyG79z26Spin98/FHgWpid++hmVGutURmpGatDb0RX8sXywLD0KCSKc0C8uxhk3pCe6mZpxhblcthCW/bl7Qmsr8GVbnp18Fk9Wfob7L+mLwd10aGesb63/NUR2fh4A9jRZ8PKAG2S3jWg4FBWWjx/WrAcAqIMcacJ6UB9Zqe5TGT3SnWG5r/W7HrtDkDeLszn9MgKP5CR3i5nA8zh1ugb3nMp1rEvJzAh+Q7oA4yXl/XlnrVly2SA6DRME4Rv6xcU4qcla/GvOGPz+1qmy210v8C5Yo17Yex4Du+g1MCrrgFLHW8VHuTYPZ/YfDll7/aFScM8J0VNptTTce8UQKDy8pQrG0FlthH070PHffzmmNr4pPAcAoFcG2b/Bg+OonNPklLJMyXKrOgWWmkoIXur/BMrK7631c1Q8Jz9/0dSAu9dJw7PTZIRSJPFWbqcvrIrNKDjvqQk1sVXtmSBiERIfcYA3k/FeNkuyfEphLSzHMAwY1jnPfWWB8wl9YO/RILcwMDKS3BPvvjhjMD69uT8yS0thgHyiLcFDzZGuwll4PPr9KdyM8Tiz5gvwhvD6xdzYR94J2LWI3c6Kelz7bQuuef8IOB8J2vyh+nQNvmhxWtN6prtbPn4sd88Lo4oyKwIjG2xrZZLeGlpuFFUG/uOB90PeJoJIdCi9eoJxXR95Z8BLB+fj86oqAMAugxoX8gIUAVZoDRYal3mVa5t2gmH7O5Y3WLJcvwIA2PLrQUwfMjzo7Xnvi63YnWXNKTK/fSCwypmQ6nZFOYD+Hr4ZOK4OpzrOgBvH9vPru9+lD3B8vum9fSji21CvSsazU0pRmCV/3U9WVGLTr4fQbrKAaW3CRef0A2sxo3DEcHyw9RQAa8r6ydW/olu/24DthyTf/8eh4FlZQoU3nw9NqtU/hROJD6Z7ScjbRBCJDomPBOPmMcWy64ty0gBYxcdPymIc/u9W/GvWCDAquSoeocXiEt556Tjp4D4uzYyNLe7tWnVGwPQQtGdVa7rHbeeOHRjUczEuobYZFkOnitaZWCWOsxmAABzbewiFF4x026fNyOEPG1oA2ByVU4rxyT4AUOG22l/xgz7Nse+cGy6CgmUwAyfQ0GrAd6nugiubNeN35/d0Wx9pvNXdS7ryegBAcYrTmsbe/Uiom0QQCU902UeJoDOqe4bj80W1OzxaMxQsgws1TY7lamUaOnZsDnHr5Gl1yaCZ4uLoeEVfeTFwUBN8Z9m2Du8OpZk95MVcZ2FU0tou2pwcr/tPrN7mcVue2VpCfsMp+aRsN6/0PL32Vn2aZFnX0yoqZt98Ge658xrZ7yy9cTDO75Emuy2SMB7Uh9ZiBJtqvZfyslLw/K8v4l+/PA2kZ8ruTxBE8CDxEee8MtNZ60XIyPa6b0GadJA/2xSZSqT1rU7H0Z5tVUjJkg4Gg/p1Rx+Vcx97Yq5BTceC1gZBECB06LHvmOfU5d3bq6FQBtl4WOS0HBSnqXHfBd4tCb89+pns+rtTzqBBaZ1q2YQ8/LTqK3A1VZ1q0hA//q7Pdm/olIUmHKiVnsSHCbD5SzFaHfr+8c/I/8sTkpwfBEGEBhIfcY6CZfDwsCQMUrRhxuVjve6bkiR9637zSPCTVvlDvcHpR/BomQWMS/4ShmHwzHVDHcuTFHUAgH0ZvYOWZtzyr2fRfN9teGqnweM+T1yQH5RziWFF01yvXFmKHhkaL3sD3c6/QFZ0KRQKcIxzEF1i6IUb19Y5lju2bvC7TQ/cOM5t3awsZ/2gT2/uj/4XuO8TLahU8gJRYzEBIqdrpmdvMMW9wtQqgkhsSHwkAGOH9MJTN45GYbcMr/upkqVOiTsz3BNbhYN6s/UN+o70WuRc6CHRmCiioqSv0zrQePSI4/O7b32KDxe/CIEPvPbK3zt6Yc74JxzL3dudNW/mqcrx/qXZyO4fPEdTO8MLrNfAX1/fjDsewL4M9zokKpm3fTNrFTaCIOCGw+5WsMvPSAXJb3AMn8zqh/Rs92mI6RePwGz1Gfyjf2jSugcTtVLekqEQeIflgyCI8EIOp4SDvIJc4IC0tPjZqrPIK8gLazvqbTlHstO854t49tKe0JstGJSnw9JD1twkLTW10KSkoqmDw0fqfkCvfrjii5VIuXKm3+dvaDdhQ95wybpaTSaWXdMLVeUn0W/QFDAhGrQm9ExFkrIYpVneLR52FOkZ8usBXN03DZ8eaXGsY21VeI0WeevQzEtGYGppCd7ZVoPphUD/PlM8TqWoVUrMmHGRX22MNCq1EpCpjcMzDEBTLAQREUj2Ew6G5uswvnuKZN3ibyvC3o561io6cjK9Oy/2z03CyMIUaJQs1Lw1E+eMTUZUn6zEN5udidLqVf47QQqCgNs+KXdbr+bNSE/Wov+QspAJD8A6pTSmOAXZuq5FGTHNjbh5pHRaqFdbFY4cPoGPP5NaOJQ8h+ENh5A5fAR6pGvwyOQeGNy/B5QeLAaxhtrDtAsPFkxydKWCJ4hEgSwfhAOWYfDABcUYcbgOL2+1+gf0NDf4+FZwMZk4NKmsAig7z/+oAxPrvJXf+2o71umcUxF1ggY9ARw7dRY//7ANN0zoi6SSPrLHeXL1LgDuGVYzhcjXvAkEkwBoXKZeNDyHP281AHCmQn9duwtpo8eA1Y6NWofRrmL1+XD3X7J4i8ElCCKk0K+PcOPcXs5BP0sT3gHpP7+cBmB9G0/L6VzIY7teOtA82VAA/enTeOr7k1itKMGNGzkYm+SzoW43uAuPHN6AP07s1am2hIPLe1vf3i9Mc/abS7P+7a6zOK04B9J7uX0394oroe1ZCnU396q58YJaI29FqtdmhLchBEE4IPFBuJGiVuB67VkAgJEPnfjgBQEvbziFT/bUONZ9c8pqYeBYJVhF5wxzv+a4J/766utfUKd0Til9vtq9AuxXn62TLF/UvB8PpJ7G0ltGoLR3cPN5BJO5Y4uw6OLumD91iGNdn3NGAQBmTz8fj7J7PX9ZGf4kcuFGrY7/PhJErEHTLoQs9rohB4z+OT52hl2nW/BtRTuAdlzZSwtFqudMor64Ob0R7zV7tpSc7eAlsyk721W4HkC7icOi1btwbjaLN1udScruqF+PqffM7XR7wolKwWBIN2uUzOtX90a93oye2dZ6MGxqOoZedxXwkbsfS4q5HQyJD4IgIgBZPghZfmyxjtTHk4KfNdTO21tOOz6vXPkDLF0ohsb7uJV3q6T92JPZF59tPY5ZHx3Ffi4Zb9ZII2suv/u2TrclkuSlqDAgT1qITqVW47fm/W77/u3cxHC2VKjdC+IRBBFZSHwQslQrQz8wVXQ4oym2GbQwd0F8bNW7+2qMqD/o+Hwm2V1ELT3sOYmauOJvPHDeCPdcIPm9ukegJREgAaw7BBFrkPggZJnCeE4rHgoOpffCxiO1nf7+5T1caqJYjCjNz+jUsRb2jq3IFn/IHToUrybtwRM9WvEkduB19TYwWu95VOIGD6HROUz0J0gjiHiFfD4IWS4ZWYKvthmRbA5dfZeS1jM4nlrkWH5xe6Pjc6+2SgRSqv6ic/ohI/0MFm63FlHrUGjQb9woYN2ZgNs1ZPSggL8T7TAMg6JrZ8D61x4T4daEF9cQ4vPMZzBq/CiMLEz28A2CIEINWT4IWTRJtmmMIOd+4ERTK5zafarEzmOjApv2YRgGw/sVSdaNLUpBvyROsu7i2h1IEeTfeIe1lGNBQWNCOGEmMkqBxyV9MrqcyI0giM5D4oOQxZ4VUpy8q6v8sv8Mbly+Hz9tsjo/2pOJuXLdie+QNTrwt3NxvRfAKkiuzpOKj959e+DpEVLR0zeFwfKre+DJu6Zi7OTzAj4vEVtYEJ/J1AgiliDxQcii0VhFh5lVwWLhfOztH0/vaIWZUWBJOQuOF9DKyofxWjJygnI+AEhVS2/xyReNQY9BZehjclZ4/dvV/ZCconP9KhFHsIKzuGDgZQYJggg2JD4IWcRZIc2mrosP11L3zR3WY9qLnYnhWlrc1vnLq1f1AwDcOtwqYEoGlTm2vZhdAa3WGnZ578VlmGQ6gVcGxJ9zKeHOH3HA8Vld3COCLSEIAiCHU8IDapUzN0K7wQwoVdCqOh9+ajQ7BczApnIcPGEt9pbMdeA/V/bCTV85I124LoS5julXhE9vdprVU3QajE7qQHtzC4onnO9Y36N7Hu677bJOn4eILS647nLUfbYe69Q98JtxJZFuDkEkPCQ+CFkUKiWUPAeOVeLONSegsZix9PoyaJI7FyGwv6bd8flQWk/s3261brSqkpGklSaBMii6llWVYRiHpYVhGDxy7fAuHY+IfZgkHa674VJcF+mGEAQBgKZdCA8wLAs1bwYAmBglWpVJOLZ+s2N7m9GCdpP7lIkn3tp+1vHZ4mrZcFluVss7ohIEQRDxAYkPwiN6pTQJ1f/VFQIA2k0W3LzyCGZ9dARtes9ZQsW0tHnxrVBKDXAjGg4H1lCCIAgipiDxQQREe5seaw7WO5ZP/rLFr+81C55zKjAqNZ7s0Yp8Qx2uO/EdLucqutpMgiAIIooh8UEExK6vf8C6Y85MpN7ygNQ3t+PrH3eiw9ABwUOysolN1pwfwyaMwb/GJeNmbTXU8x4KbqMJgiCIqILEB+GRQU3HAAALs5zVZ43bN6O+xZly3WT27Pfx5Kd78OoZLW7/YC/SePlpl+tHFjo+MwNHQHH/QjAFCVLwjCAIIkEh8UF45PGLeuH1wjMYMuUijBCsSbn+MXCWxBfEZJFP2SQIAioUGQCAFnUKWlhrVtHf6nehR1uVY7/uYxOrzghBEARBobaEF9T9ByGvv7XImlatBMzu+5g4efGx+L31AJPrtj6jsBvazjqziTKK+CpdTxAEQfgmYPGxf/9+fPbZZzh+/DgaGxvx5z//GWPHjnVsFwQBK1aswHfffYf29nb0798fc+fORUFBQVAbToSXJFaQXW+yyK/fKiM8AECpYHGT6SBeUY+1Tev4X7mWIAiCiA8CnnYxGo3o1asXfve738lu//TTT/Hll1/i9ttvx1NPPQWNRoPFixfDZJKvJErEBjoPBgoT7y4+qlo9h98qFSwuvmoyXj78Jp4YSLN+BEEQiUjAlo8RI0ZgxIgRstsEQcCaNWtw7bXXYswY61z+3Xffjdtvvx1bt27F+eefL/s9IvpJ8qATTJy7+HhvfTngoXKoQsGCLShGjyeeC2LrCIIgiFgiqD4fZ8+eRVNTE4YOHepYp9Pp0KdPHxw+fFhWfJjNZpjNTmcChmGQlJTk+BxM7McL9nGjlWD2t9FDkjAT737805V1gNbTtIsiZH9/ur7xT6L1mfob3yRaf8UEVXw0NTUBANLT0yXr09PTHdtcWb16NVauXOlYLikpwbPPPovcXPnBKxjk5+eH7NjRSDD6e4ZNddQivxDVyNIo8bExB1CqJP48HM/juEh43H3wQ7zR9xp02Oq1ZOi0Iff/oesb/yRan6m/8U2i9ReIgmiX6dOnY9q0aY5luwKsra0Fx3W9lLsYhmGQn5+P6upqtxLv8Ugw+9tTaMUBWGuuZOgbAaVVYLYZzaiqcobOnmiUWkguuX8+Nn91BFt5q/ior66R7B9M6PrGP4nWZ+pvfBNv/VUqlX4bDoIqPjIyMgAAzc3NyMzMdKxvbm5Gr169ZL+jUqmgUsmn3g7VxRAEIS4utL8Eo7+zLh6Cr76x5vq44oJB+H63NfGYiZdep7raBsfn7u3VQGZ/aFPOANYitig7f0zI//Z0feOfROsz9Te+SbT+AkFOMpaXl4eMjAzs2bPHsU6v1+Po0aMoKysL5qmIMJOel4PVN/bBhzN6I7dfGVQKq4VqrzlZst+vG3Y4Pv+pl9WXZ9bE/khhLLimkEFqViYIgiCIxCZgy0dHRweqq6sdy2fPnkVFRQVSUlKQk5ODqVOnYtWqVSgoKEBeXh4++OADZGZmOqJfiNiFVSihtYXcHuuwfqhSZ8BkNEGtUQMAvlCVOvYvufQSAEBhmgbv3jQwIZ2qCIIgCHcCFh/Hjh3DE0884VhetmwZAGDixImYP38+rr76ahiNRvz73/+GXq9H//798de//hVqtTp4rSYizhBDJdZr8wAA77zwDm5/6HYAwEC+EfvZTJToqyFOIEbCgyAIgrATsPgYNGgQVqxY4XE7wzC44YYbcMMNN3SpYUR0M7mjHK9phwMA/td9AmY2tsCi1mA/a51WmaBrj2DrCIIgiGiGUkwSnUJ1zWxccXq9Y/k3ayrxwc+HHcuW2ppINIsgCIKIAUh8EJ2CKeqBC6+bIln3db0zamnwufJZcAmCIAiCxAfRaThGvuBLprEFA0l8EARBEB4g8UF0mtKeeSjgWtzWjxDqItAagiAIIlYg8UF0Go1SgVd/4x5Cfeu1VECQIAiC8AyJD6JLsAyDe4anSdalZ6Z72JsgCIIgSHwQQeCiQYWOz6xgiWBLCIIgiFiAxAcRFP55QSZ6d5zFg7kNvncmCIIgEpqIV7Ul4oPu3bvh77/rFulmEARBEDEAWT4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrUVtYTqkMXdNCeexohPob3yRaf4HE6zP1N76Jl/4G0g9GEAQhhG0hCIIgCIKQkFDTLgaDAX/5y19gMBgi3ZSwQP2NbxKtv0Di9Zn6G98kWn/FJJT4EAQBx48fR6IYe6i/8U2i9RdIvD5Tf+ObROuvmIQSHwRBEARBRB4SHwRBEARBhJWEEh8qlQrXX389VCpVpJsSFqi/8U2i9RdIvD5Tf+ObROuvGIp2IQiCIAgirCSU5YMgCIIgiMhD4oMgCIIgiLBC4oMgCIIgiLBC4oMgCIIgiLBC4oMgCIIgiLASN+LDYDBIssTFexCPyWSKdBPCSnV1NaqrqwEAFoslwq0JPVVVVfjss89QWVkZ6aaEjaamJjQ0NDjubZ7nI9yi0JJov+FE+N2Kqa2tRX19PYD4v5c7Q8yX0uM4Dm+++SZOnTqFlJQUTJgwAePGjQPDMJFuWkjgOA7vvvsu6urqoNVqcdFFF2HAgAGRblZI2bt3LxYuXIiePXviueeeg0KhiHSTQgbP83jzzTfxww8/YPz48SgrK0NhYWGkmxVS7L/hXbt2ISUlBUlJSfjrX/8KtVod6aaFBI7j8NZbb6G2thZpaWm49NJL0bdv37h+Zn3wwQdoaWlBcnIypkyZgm7dukW6WSFl69at+Nvf/oZRo0bhwQcfBMvGzXt+0Ijpv0h7ezuefPJJnDp1ClOmTIHFYsGHH36Id955J9JNCwlbtmzBH/7wB5w4cQKDBg1CRUUFli9fjl9++SXSTQsplZWVGDBgAFpaWvDtt98CiN+3qP/97384ceIEHn/8cdx1113o378/gPi15DU0NOCxxx5DVVUV7r33XkydOhX19fVYvnx5pJsWEpqamrBgwQKcPHkSo0aNwokTJ/D666/js88+AxB/b8ibNm3C/PnzcezYMWRnZ2Pjxo14/fXXcejQoUg3LaQcPXoUffr0QX19veP5HG/XtqvEtOXjxIkTaG5uxp/+9Cf06NEDY8eOxYYNG/Daa69h2LBhGD58eKSbGDSqq6vx888/48ILL8TMmTMBAOeffz5eeOEFx3REvCEIAhiGQW1tLQoKCjBw4EB8/PHHmDRpEpRKpWN7PCAIAoxGI7Zs2YILL7wQffv2xeHDh3Hy5EkUFxejV69e0Gq1kW5m0Dlw4ABMJhMeeOABZGRkoKysDHv27InLvgLAwYMHwXEc/vKXvyArKwsTJkzAF198gRUrVmDkyJHo3r173NzXFRUV+PHHH3H55ZfjmmuuAQBcfPHFeOyxx1BbW4t+/fpFtoEhgOd5sCwLvV6P3r17w2Qy4csvv8To0aPj7pnVVWLa8tHa2or6+nr06NEDgDVV7cSJEzF+/Hj897//jYs5VfsbL8dx6NmzJyZNmgTAepOnpaWBZdm4FR/2H2lLSwtGjhyJc889FwqFAitWrAAAGI3GSDYvqDAMg8bGRtTU1GD48OFYtmwZlixZgnXr1mHJkiV4/vnnodfrI93MoNPe3o7q6mpkZGQAABobG3Hy5EmkpKTg4MGDkW1cELG/9ba0tKCtrQ1ZWVkAAJ1Oh0suuQT9+/fHf/7zHwCIm8GJ4zgUFRXhggsucCxnZ2cjOTkZp0+fjnDrQgPLshAEAdXV1bjgggswduxYtLa2Yu3atQDi12LbGWJGfBw9ehSA1HSVlJSE7Oxsh1nLripnzJiB6upqbN682e07sYK9v3bxUVxcjOuvvx55eXkArDc5x3EwmUwoKyuLWDuDhdz1tfe9vb0dRqMRhYWFuOaaa7B27Vq89NJLWL58OVpbWyPS3q4i19/s7Gykpqbigw8+QG1tLR555BE8+OCDeOSRR1BeXo5Vq1bF9PSLXJ/Lysqg0+nw17/+FUuWLMG8efOg0+mwY8cOPP3001i5ciU4jotUk7vEL7/8gt27d6OxsdEx58+yLDIyMnDgwAHHfhkZGbjmmmtw7Ngx7N69G0BsTrPZ+9vQ0AAA6NOnD37zm984hJZSqYRer4fRaHRMJ8Yy4utrh+d5MAwDlmVhNpvRt29fjB07Fj/88ANeeukl/O9//4PZbI5gq6OHqJ922bJlC5YuXQqO4/D0008jLy8PFosFCoUC+fn5yMnJwZ49ezB8+HBotVrwPI/c3Fycf/75+PbbbzFhwoSYcvaR66/dlAdAYrbjOA4tLS3o3r17JJvcJbz1l2EYmM1mnDp1CrfffjuUSiWamppgNpuxefNmLFy4ECkpKZHuQkB4669dSG7atAlDhgxxOJqmpqbilltuwbJlyzBz5syYc8T09hvu1asXFi5ciMrKSixbtgx33XWX4035559/xn/+8x9ceOGFyM7OjnAv/Oenn37Cf//7X+Tm5uLs2bMoKCjAtGnTcM4556B3795Ys2YNDh06hL59+0KptD6Cu3fvjuHDh+Onn37C0KFDY8r6IdffK6+8EmPHjoUgCBAEwfH8am9vhyAIyM/Pj3CrO4+3/rIsi7a2Nhw/ftxxfY1GIyorK1FVVYVp06YlZBE5OaJ6VP7555+xevVqDBgwAEVFRfjkk08AAAqFAoIgIC8vDwMHDsTx48exZcsWANY3C4VCgZSUFKhUKnR0dESwB4Hhqb9i8SR+KB08eBAdHR0oKChwrGtqagpXc7uMr/7yPA9BEFBSUoL169fjwQcfxFdffYVx48ZBrVZDr9eDYZiYMWX66m9KSgoGDx4MpVIpEZyAdXBSKpUxZ6729hu2k5eXh7a2NrAsiwkTJjgsI/379wfHcThx4kQkmh4wFosFa9aswerVq3HTTTfhySefxAMPPIBu3brh+++/h8lkQklJCfr3748tW7ZInC4zMjKgUChiSnR46+93330Hs9nssALYLTn79u0DAIc1BADa2toi0v5A8ae/gDWEeuDAgdi8eTP+/Oc/46effsKQIUOQm5vruLdj0RofbKJSfNgvTH5+PoYMGYLZs2dj9OjR2L9/v+PmtQ84l156KbKysvDdd99JciI0NzcjMzMzJhzX/Omv3M26ZcsWDBw4ECkpKTh+/DieeOIJvPHGG1F/Y/vbX5Zl0dHRga1bt2L58uXo378//v73v+OWW27B0KFD8dJLLwFA1Ife+tNf+9TC6NGjMWHCBGzbtg27d+92CJCDBw+iV69e6NWrV0T6ECiduacZhkFzc7Ojz9u3b0dpaSn69OkT3sZ3EqPRiJaWFkycONHhFN2vXz8UFxdDr9c7rvHMmTNhsVjw7bffOqYoAOugFUuWPF/9Fb8U2EXV1q1bMWLECKjValRUVGDRokVYuXJlTEwz+dtfnuexadMm/POf/8SAAQPw0ksvYfbs2cjNzcWyZcsAIKas8aEiqqZdqqqqkJ+f77gwffv2RWlpKRQKBUaMGIGDBw/is88+w6BBg6BUKmGxWJCamopp06Zh9erVWLBgASZPnozGxkbs3LkTd999NwBErYdxIP21vz3Y+8HzPJqamtC/f3+8+eab+PrrrzFhwgTceeedUXtjB9pfjuOQlpaG+++/HxkZGejdu7fjWJMnT0aPHj0cD61Yv752a4dWq8XUqVPR1taG559/HsOGDYNKpcKuXbswa9Yst/sg2gj0GttFZlpaGpKTk7Fw4UJMmTIFR44cwa+//orrrrsOaWlpEe6VZ+z9ZRgGOp0O5557Lnr06CHpW05ODoxGo2O6LCMjA9OnT8eXX36JRx55BJdffjkqKipQXl6O6dOnR7hH3gmkv/YpJTsdHR0wGAzo27cv3njjDXz77bcYN24cZs+eHfX3cyD9zcnJwb333ou8vDyHcE5OTsaYMWMkyTCjtc/hghGiQHJu3LgR7733HlQqFXQ6HS6++GJMnjwZgFQ4/PDDD/j8889x5ZVX4sILL3TMGwOA2WzGxx9/jLq6OnR0dGDWrFlRm5yps/0Vm+Lr6uowf/58AFanvTvuuAPFxcWR6ZAPgnF97dj3j+YBOFj9/eabb1BTU4OWlhZcc801UXs/A8Hp86FDh/DJJ5+A4zioVCrMnj07avvsrb8AJL/Vl156CUqlEvPmzQPHcY5BqqGhAStXrkRzczM4jsOcOXPirr/i61tRUYG//OUvAKzOqHfddVdMPrMA/66vHfv97zqVmuhE3PKxe/duvPfee7jqqqvQrVs37N69G6+//jp4nscFF1wAtVrtuIGHDRuGQ4cOYe3atTjvvPOg1WodF1ulUuHGG2+M+gscrP4aDAacd955mDx5MoYOHRrpbnkkWP21Yx/EolV4BLO/l1xySYR74x9d7bPZbIZKpUK/fv3wwAMPoKOjAzqdLtLd8og//bULZLvD9JVXXgkAkns5KysLv//972EymaLaibgr/RWLaYPBgIEDB2L69Okx/8zy5/qKHecBmmpxJWLiw64GDx8+jNTUVFx00UVQKpUYPnw4TCYTvvvuO6SlpWHs2LGOGzgrKwtjx47FiRMn8Nlnn+Gcc87B+++/j7lz5yInJwdA9F7gYPe3e/fu+OMf/xjZTnkhVNc3Wkm0/gKh6TPLslErPALpr33AaWtrg16vR9++fQFYzfhr167FnDlzHMeNVuERrP5+/fXXuPXWWzFgwAA89thjkeySV4J9faN1LIoWIvbXsV+806dPo1u3blAqlQ6HrBtvvBEqlQpbt251RG/YndMGDRqE3r174+OPP8ZDDz0Ei8WC9PT0iPQhEKi/1N946i+QeH0OtL8AsGfPHuTk5CAzMxNvvfUW7r//ftTW1oLjuKh3sgxWf+vq6sBxXNQ7wifa9Y00YbN87N69G7/++iu6deuGfv36ORxxBg8ejP/+97/ged5xsVNSUnDBBRfg888/R2VlJTIyMhyRD9999x2+/fZbDBw4ELfddpsju2m0Qf2l/sZTf4HE63Nn+3vmzBlkZGRAEARs27YNJ0+exPz585GRkYFFixZJHKejCepvfPc32gi55aOxsRHPPPMMXn75ZbS1teGHH37AokWLHNkOBw4ciKSkJHz00UeS71188cUwGAw4fvy4Y11dXR02btyIefPm4bHHHovKhxb1l/obT/0FEq/PXe1vRUUFAGvorMlkglarxe9+9zssWbIkKgcm6m989zdaCanlw2g0Yvny5dBqtVi8eLEjNfhf//pXrF27Fn369EFmZiYuvfRSrFq1ChdddBFycnIcc2+FhYU4deqU43jFxcVYvHhxKJvcJai/1N946i+QeH0OZn81Gg1mzpyJ0tLSSHbJK9Tf+O5vNBNSy4dGo4FKpcKkSZMcKZUBYMSIEThz5gwEQUBSUhLGjx+PkpISvPDCC6itrQXDMKirq0NzczPGjh0byiYGFeov9Tee+gskXp+D3d9oH5iov/Hd32gm5Hk+xKGD9tCjl156CRqNBnfccYdjv4aGBjz++OOwWCzo3bs3Dh06hKKiItxzzz2OipexAPWX+gvET3+BxOsz9Zf6C8RPf6OViCQZe+SRR3DRRRdh0qRJDg9oe2n48vJyHDlyRFI+Ptah/lJ/46m/QOL1mfpL/Y2n/kYDYc/zUVNTg+rqaoejmT2NNsuyyM/PR35+PsaNGxfuZoUM6i/1N576CyRen6m/1N946m+0ELY8H3YDy8GDB6HVah1zZR999BHeeustNDc3h6spYYH6S/2NNxKtz9Rf6i8ROsJm+bAncDl69CjOOecc7N69G//+979hMplw9913x0SSoUCg/lJ/441E6zP1l/pLhI6wTruYTCbs2rULNTU1+PLLLzFjxgxcc8014WxCWKH+Un/jjUTrM/WX+kuEhrCKD7VajdzcXAwdOhS/+c1voramQbCg/lJ/441E6zP1l/pLhIawR7tEe9XZYEP9jW8Srb9A4vWZ+hvfJFp/o4WIhNoSBEEQBJG4kNwjCIIgCCKskPggCIIgCCKskPggCIIgCCKskPggCIIgCCKskPggCIIgCCKskPggCIIgCCKskPggCIIgCCKskPggCKJTzJw5EytWrAjrOefPn49XXnklrOckCCL4kPggiDjg0KFDWLFiBdrb2yPdlKikoaEBK1asQEVFRaSbQhAESHwQRFxw6NAhrFy5ksSHBxobG7Fy5UoSHwQRJZD4IIgEgud5mEymSDeDIIgEJ6xVbQmCCD4rVqzAypUrAQB33323Y/0///lP3H333bjssstQVlaG1atXo6qqCvfddx/Gjh2Lzz77DFu2bEFlZSWMRiOKi4sxffp0nHvuuZLjm81mvPfee/j5559hNpsxaNAgzJ07V7YtDQ0N+OCDD7Bjxw60t7cjPz8f06ZNw+TJkwPqkyAIWLVqFb755hu0tbWhb9+++O1vf+u2X1tbG1atWoVdu3bh7NmzYFkW/fr1w6xZs9CrVy8AwL59+/DEE08AAF599VW8+uqrAIB58+Zh0qRJAIAjR45gxYoVOHz4MCwWC3r37o2bbroJ/fv3D6jdBEH4B4kPgohxzjnnHFRVVWHDhg2YM2cOUlNTAQBpaWkAgL1792LTpk2YMmUKUlNTkZeXBwD48ssvMWrUKIwfPx4cx2Hjxo34+9//joceeggjR450HP9f//oXfv75Z4wfPx5lZWXYu3cvnnnmGbd2NDU1YcGCBQCAyy67DGlpadi5cyf+9a9/wWAw4IorrvC7Tx9++CFWrVqFESNGYMSIETh+/DgWLVoEjuMk+9XU1GDr1q0477zzkJeXh6amJnz77bd4/PHH8fe//x1ZWVkoKipyOMdefPHFDkHRr18/x9/nqaeeQmlpKWbMmAGGYfDjjz/iySefxJNPPok+ffr43W6CIPxEIAgi5vn000+FGTNmCDU1NZL1M2bMEGbOnCmcOnXK7TtGo1GybDabhfvvv1944oknHOuOHz8uzJgxQ3j99dcl+7744ovCjBkzhA8//NCx7rXXXhN+//vfCy0tLZJ9X3jhBWHOnDlu5/NEc3OzcNNNNwlPP/20wPO8Y/3y5cuFGTNmCP/85z8d60wmk2CxWCTfr6mpEWbNmiV89NFHjnVHjx4VZsyYIfzwww+SfXmeF+655x5h0aJFknMZjUZh/vz5wsKFC/1qM0EQgUE+HwQR5wwcOBDFxcVu69VqteNzW1sb9Ho9BgwYgOPHjzvW79ixAwAwdepUyXddlwVBwObNmzFq1CgIgoCWlhbHv+HDh0Ov16O8vNyv9u7evRscx2HKlClgGMaxXs5yolKpwLLWxxjP82htbYVWq0VhYaGkH56oqKhAVVUVxo8fj9bWVkebOzo6MHjwYBw4cAA8z/vVboIg/IemXQgizrFPs7iybds2rFq1ChUVFTCbzY714gG/trYWDMOgW7duku8WFhZKlltaWtDe3o5vv/0W3377rez5Wlpa/GpvXV0dAKCgoECyPi0tDcnJyZJ1PM9jzZo1WLt2Lc6ePSsRCikpKT7PVVVVBQBec4fo9Xq/jkUQhP+Q+CCIOEds4bBz4MABPPfccxgwYAB+97vfITMzEwqFAj/++CPWr18f8DkEQQAATJgwARMnTpTdp2fPngEf1xerV6/Ghx9+iAsvvBA33HADUlJSwDAM3nnnHUebvGHfZ/bs2Q4HVVe0Wm0wm0wQBEh8EERcILZW+MPmzZuhUqmwYMECqFQqx/off/xRsl9ubi4EQUBNTY3E2lFZWSnZLy0tDUlJSeB5HkOHDg28AyJycnIAWK0SYouL3boi5pdffsGgQYNw1113Sda3t7c7HG8Bz38f+/F1Ol2X200QhP+QzwdBxAEajQaAdYrAH1iWBcMwkmmKs2fPYuvWrZL9RowYAQBYs2aNZL3rMsuyOOecc7B582acPHnS7Xz+TrkAwNChQ6FQKPDVV19JrBdffPGFbD9c2bRpExoaGiTr7H8fV/FSWlqKbt264fPPP0dHR0eX2k0QhP+Q5YMg4oDS0lIAwPvvv4/zzz8fCoUCo0aN8rj/yJEj8b///Q9PPfUUzj//fLS0tODrr79Gfn4+Tpw44divV69eOP/887F27Vro9Xr069cPe/bsQU1NjdsxZ82ahX379mHBggW46KKLUFxcjLa2NpSXl2PPnj146623/OpLWloarrzySnzyySd45plnMGLECFRUVGDHjh0SawYAjBo1CitXrsSrr76KsrIynDx5EuvXr3fzUenWrRuSk5PxzTffICkpCRqNBn379kVeXh7uvPNOPPXUU7j//vsxadIkZGVloaGhAfv27UNSUhIeeughv9pNEIT/kOWDIOKAPn364IYbbsCJEyfwyiuv4MUXX/T61j548GDceeedaGpqwjvvvIMNGzbg5ptvxpgxY9z2veuuu3D55Zdj165dePfdd2GxWGQH5IyMDDz11FOYNGkSNm/ejKVLl2LNmjVob2/HzTffHFB/brzxRsycORMVFRV49913UVNTg4cfftjN/2L69OmYNm0adu3ahbfffhvHjx/HQw89hOzsbMl+SqUS8+fPB8uyeP311/Hiiy9i//79AIBBgwZh8eLFKC0txddff4233noL69atQ0ZGBqZNmxZQuwmC8A9G8McriyAIgiAIIkiQ5YMgCIIgiLBCPh8EQYSNlpYWr0m7lEol5dQgiASApl0Igggb8+fPR21trcftAwcOxOOPPx6+BhEEERFIfBAEETYOHjwIk8nkcXtKSoojcocgiPiFxAdBEARBEGGFHE4JgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggrJD4IgiAIgggr/w/KhTRLhpongwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          36.50  39.12\n",
       "1          36.66  37.39\n",
       "2          38.15  38.47\n",
       "3          36.80  37.96\n",
       "4          34.87  38.02\n",
       "...          ...    ...\n",
       "3547       43.20  43.98\n",
       "3548       45.21  45.24\n",
       "3549       46.30  46.58\n",
       "3550       45.47  46.68\n",
       "3551       45.04  45.50\n",
       "\n",
       "[3552 rows x 2 columns]>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>45.04</td>\n",
       "      <td>45.50</td>\n",
       "      <td>44.21</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>45.47</td>\n",
       "      <td>46.68</td>\n",
       "      <td>44.93</td>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>46.30</td>\n",
       "      <td>46.58</td>\n",
       "      <td>45.20</td>\n",
       "      <td>45.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>45.21</td>\n",
       "      <td>45.24</td>\n",
       "      <td>42.73</td>\n",
       "      <td>43.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>43.20</td>\n",
       "      <td>43.98</td>\n",
       "      <td>42.51</td>\n",
       "      <td>43.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>34.87</td>\n",
       "      <td>38.02</td>\n",
       "      <td>34.87</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>36.80</td>\n",
       "      <td>37.96</td>\n",
       "      <td>36.38</td>\n",
       "      <td>37.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>38.15</td>\n",
       "      <td>38.47</td>\n",
       "      <td>35.91</td>\n",
       "      <td>36.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>36.66</td>\n",
       "      <td>37.39</td>\n",
       "      <td>35.80</td>\n",
       "      <td>36.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>36.50</td>\n",
       "      <td>39.12</td>\n",
       "      <td>36.42</td>\n",
       "      <td>38.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3552 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  45.04  45.50  44.21  45.40\n",
       "14614.0  45.47  46.68  44.93  46.25\n",
       "14615.0  46.30  46.58  45.20  45.30\n",
       "14616.0  45.21  45.24  42.73  43.21\n",
       "14617.0  43.20  43.98  42.51  43.39\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  34.87  38.02  34.87  37.80\n",
       "20077.0  36.80  37.96  36.38  37.38\n",
       "20080.0  38.15  38.47  35.91  36.15\n",
       "20081.0  36.66  37.39  35.80  36.81\n",
       "20082.0  36.50  39.12  36.42  38.09\n",
       "\n",
       "[3552 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>45.04</td>\n",
       "      <td>45.50</td>\n",
       "      <td>44.21</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>45.47</td>\n",
       "      <td>46.68</td>\n",
       "      <td>44.93</td>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>46.30</td>\n",
       "      <td>46.58</td>\n",
       "      <td>45.20</td>\n",
       "      <td>45.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>45.21</td>\n",
       "      <td>45.24</td>\n",
       "      <td>42.73</td>\n",
       "      <td>43.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>43.20</td>\n",
       "      <td>43.98</td>\n",
       "      <td>42.51</td>\n",
       "      <td>43.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>34.87</td>\n",
       "      <td>38.02</td>\n",
       "      <td>34.87</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>36.80</td>\n",
       "      <td>37.96</td>\n",
       "      <td>36.38</td>\n",
       "      <td>37.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>38.15</td>\n",
       "      <td>38.47</td>\n",
       "      <td>35.91</td>\n",
       "      <td>36.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>36.66</td>\n",
       "      <td>37.39</td>\n",
       "      <td>35.80</td>\n",
       "      <td>36.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>36.50</td>\n",
       "      <td>39.12</td>\n",
       "      <td>36.42</td>\n",
       "      <td>38.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3552 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  45.04  45.50  44.21  45.40\n",
       "1     14614.0  45.47  46.68  44.93  46.25\n",
       "2     14615.0  46.30  46.58  45.20  45.30\n",
       "3     14616.0  45.21  45.24  42.73  43.21\n",
       "4     14617.0  43.20  43.98  42.51  43.39\n",
       "...       ...    ...    ...    ...    ...\n",
       "3547  20076.0  34.87  38.02  34.87  37.80\n",
       "3548  20077.0  36.80  37.96  36.38  37.38\n",
       "3549  20080.0  38.15  38.47  35.91  36.15\n",
       "3550  20081.0  36.66  37.39  35.80  36.81\n",
       "3551  20082.0  36.50  39.12  36.42  38.09\n",
       "\n",
       "[3552 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f4d58dff50>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
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